Recall

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Definition

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Taxonomy

• Task types: Tabular classification, text classification.
• Availability: development and monitoring.

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Why it matters

• Recall measures how many of the actual positive cases the model correctly identifies, making it crucial when missing positive cases is costly.
• It’s particularly important in applications like medical diagnosis, fraud detection, or safety systems where failing to detect positive cases can have serious consequences.
• Higher recall values indicate better model performance, with 1.0 representing no false negatives.
• Recall complements precision to provide a complete picture of model performance, especially in the context of the precision-recall trade-off.

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Required columns

• Predictions: The predicted class labels from your classification model
• Ground truths: The actual/true class labels

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Test configuration examples

[
  {
    "name": "Recall above 0.8",
    "description": "Ensure that the recall is above 0.8",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "measurement": "recall",
        "operator": ">",
        "value": 0.8
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]

[
  {
    "name": "Recall above 0.8",
    "description": "Ensure that the recall is above 0.8",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "measurement": "recall",
        "operator": ">",
        "value": 0.8
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]

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Related

• Precision test - Measure positive prediction accuracy.
• F1 test - Harmonic mean of precision and recall.
• Geometric mean test - Alternative balanced metric.
• Accuracy test - Overall classification correctness.
• Aggregate metrics - Overview of all available metrics.