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Definition

The ROC AUC test measures the macro-average of the area under the receiver operating characteristic curve score for each class, treating all classes equally. For multi-class classification tasks, it uses the one-versus-one configuration. ROC AUC evaluates the model’s ability to distinguish between classes across all classification thresholds.

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Taxonomy

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

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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

To compute this metric, your dataset must contain the following columns:
  • Prediction probabilities: The predicted class probabilities from your classification model
  • Ground truths: The actual/true class labels
ROC AUC requires predicted probabilities, not just class labels. Ensure your model outputs probability estimates for each class.

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Test configuration examples

If you are writing a tests.json, here are a few valid configurations for the ROC AUC test: 
[
  {
    "name": "ROC AUC above 0.85",
    "description": "Ensure that the ROC AUC score is above 0.85",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "rocAuc",
        "operator": ">",
        "value": 0.85
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]