F2 Score Measure
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An F2 Score Measure is an evaluation measure that weights recall twice as heavily as precision in harmonic mean calculation for retrieval performance assessment.
- AKA: F-Beta Score Measure with Beta=2, Recall-Weighted F-Measure, F2 Measure.
- Context:
- It can typically compute Weighted Harmonic Means with beta parameter of 2.
- It can typically emphasize Recall Performance over precision performance.
- It can typically evaluate Information Retrieval Systems with completeness priority.
- It can often assess Legal Retrieval Tasks where missing relevant documents are costly.
- It can often measure Medical Diagnosis Systems where false negatives are critical.
- It can often evaluate Safety-Critical Systems with high recall requirements.
- It can often compare Retrieval Algorithms with different precision-recall tradeoffs.
- It can range from being a Micro-Averaged F2 Score to being a Macro-Averaged F2 Score, depending on its aggregation method.
- It can range from being a Binary F2 Score to being a Multi-Class F2 Score, depending on its classification type.
- It can range from being a Document-Level F2 Score to being a Passage-Level F2 Score, depending on its evaluation granularity.
- It can range from being a Weighted F2 Score to being a Unweighted F2 Score, depending on its class importance.
- ...
- Examples:
- Legal Retrieval F2 Scores, such as:
- COLIEE 2025 Task 3 F2 Score, evaluating statute retrieval completeness.
- E-Discovery F2 Score, measuring relevant document recall.
- General F2 Score Applications, such as:
- ...
- Legal Retrieval F2 Scores, such as:
- Counter-Examples:
- F1 Score, which equally weights precision and recall.
- F0.5 Score, which weights precision twice as heavily as recall.
- Precision Metric, which ignores recall entirely.
- See: F1 Score, F-Beta Score, Precision-Recall Tradeoff, Evaluation Measure, Information Retrieval Evaluation, Legal Statute Retrieval Task, Harmonic Mean, Legal Evaluation Measure.