> ## 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**: development (Continuously evaluate your models and datasets as you iterate on their versions.)
  and monitoring (Monitor a model in production, measure its health, check for drifts and set up alerts.).

## 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.

## Test configuration examples

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

```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",
        "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",
        "measurement": "rocAuc",
        "operator": ">",
        "value": 0.85
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]
```

## Related

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


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