ROC AUC

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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

• ROC AUC provides a threshold-independent measure of classification performance, evaluating the model’s discriminative ability across all possible decision thresholds.
• It’s particularly useful for comparing models and understanding their ranking performance, regardless of the specific classification threshold chosen.
• Higher ROC AUC values indicate better model performance, with 1.0 representing perfect discrimination and 0.5 representing random performance.
• This metric is especially valuable when you need to understand the trade-offs between true positive rate and false positive rate.

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Required columns

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

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Test configuration examples

[
  {
    "name": "ROC AUC above 0.85",
    "description": "Ensure that the ROC AUC score is above 0.85",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "measurement": "rocAuc",
        "operator": ">",
        "value": 0.85
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]

[
  {
    "name": "ROC AUC above 0.85",
    "description": "Ensure that the ROC AUC score is above 0.85",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "measurement": "rocAuc",
        "operator": ">",
        "value": 0.85
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]

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Related

• Log loss test - Probabilistic measure of classification performance.
• Accuracy test - Overall classification correctness.
• Precision test - Measure positive prediction accuracy.
• Recall test - Measure ability to find all positive instances.
• Aggregate metrics - Overview of all available metrics.