> ## 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.

# Log loss

> Learn how to use the log loss test

## Definition

The log loss test measures the dissimilarity between predicted probabilities and the true distribution. Also known as cross-entropy loss or binary cross-entropy (in the binary classification case), it evaluates how well the model's predicted probabilities match the actual class labels.

## 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

* Log loss provides a probabilistic measure of classification performance, considering not just correctness but also confidence in predictions.
* It heavily penalizes confident wrong predictions, making it sensitive to model calibration and overconfidence.
* Lower log loss values indicate better model performance, with 0 representing perfect probability predictions.
* This metric is particularly valuable when you need well-calibrated probability estimates, not just class predictions.

## 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]
>
> Log loss 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 log loss test:

```json Development theme={null}
[
  {
    "name": "Log loss below 0.3",
    "description": "Ensure that the log loss is below 0.3",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "measurement": "logLoss",
        "operator": "<",
        "value": 0.3
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]
```

```json Monitoring theme={null}
[
  {
    "name": "Log loss below 0.3",
    "description": "Ensure that the log loss is below 0.3",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "measurement": "logLoss",
        "operator": "<",
        "value": 0.3
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]
```

## Related

* [ROC AUC test](https://docs.openlayer.com/tests/catalog/roc-auc.md) - Area under the receiver operating characteristic curve.
* [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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