> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# ROC AUC

> Learn how to use the ROC AUC test

## 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.

## Taxonomy

* **Task types**: Tabular classification, text classification.
* **Availability**: <Tooltip tip="Continuously evaluate your models and datasets as you iterate on their versions.">development</Tooltip>
  and <Tooltip tip="Monitor a model in production, measure its health, check for drifts and set up alerts.">monitoring</Tooltip>.

## 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.

## 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

<Note>
  ROC AUC requires predicted probabilities, not just class labels. Ensure your
  model outputs probability estimates for each class.
</Note>

## Test configuration examples

If you are writing a `tests.json`, here are a few valid configurations for the ROC AUC test:

<CodeGroup>
  ```json Development theme={null}
  [
    {
      "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"
    }
  ]
  ```

  ```json Monitoring theme={null}
  [
    {
      "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": "monitoring",
      "usesProductionData": true,
      "evaluationWindow": 3600,
      "delayWindow": 0,
      "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
    }
  ]
  ```
</CodeGroup>

## Related

* [Log loss test](/tests/catalog/log-loss) - Probabilistic measure of classification performance.
* [Accuracy test](/tests/catalog/accuracy) - Overall classification correctness.
* [Precision test](/tests/catalog/precision) - Measure positive prediction accuracy.
* [Recall test](/tests/catalog/recall) - Measure ability to find all positive instances.
* [Aggregate metrics](/tests/performance/aggregate-metrics) - Overview of all available metrics.