F1 score
The harmonic mean of precision and recall for the same positive class.
What does the F1 score combine?
The F1 score combines precision and recall for the same positive class as their harmonic mean. One formula is 2 × TP / (2 × TP + FP + FN). TP means true positives, FP false positives and FN missed positive cases. True negatives do not enter this formula. If TP, FP and FN are each zero, the ratio is undefined; reporting must make the presentation of this special case clear.
In the fictional example with 50 service requests, there are 18 true positives, 6 false positives and 2 missed technical requests. Precision is 75 % and recall 90 %. This gives 36 / 44, or 81.8 % rounded. The F1 score summarises both metrics but does not weight the operational consequences of wrongly routed and missed requests. Separate error counts and the resulting handling needs therefore remain visible when making a decision.
Sources
Related terms
- LLM evaluationLLM evaluation tests model outputs against defined tasks and criteria. One good example does not establish overall quality.
- Test data in machine learningData reserved for final assessment whose results were not used to select the model variant being assessed.
- PrecisionThe share of actually positive cases among all cases the model marks as positive.
- RecallThe share of actually positive cases that the model correctly identifies as positive.
- Confusion matrixA table comparing reference classes with model predictions and counting correct and incorrect assignments.
- Re-rankingRe-ranking reassesses search candidates already retrieved. Relevance to the query and the limits of the initial selection both matter.
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