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Definition

The geometric mean test measures the geometric mean of the precision and the recall. This metric provides a balanced measure that considers both precision and recall performance, calculated as the square root of their product.

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Taxonomy

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

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Why it matters

  • Geometric mean provides an alternative to F1 score for balancing precision and recall, particularly useful when both metrics are equally important.
  • It’s more sensitive to low values than arithmetic mean, making it effective at identifying models with poor performance in either precision or recall.
  • Higher geometric mean values indicate better balanced performance, with 1.0 representing perfect precision and recall.
  • This metric is particularly valuable for imbalanced datasets where you want to ensure good performance on both classes.

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Required columns

To compute this metric, your dataset must contain the following columns:
  • Predictions: The predicted class labels from your classification model
  • Ground truths: The actual/true class labels

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Test configuration examples

If you are writing a tests.json, here are a few valid configurations for the geometric mean test: 
[
  {
    "name": "Geometric mean above 0.75",
    "description": "Ensure that the geometric mean of precision and recall is above 0.75",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "geometricMean",
        "operator": ">",
        "value": 0.75
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]