> ## 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.

# Root mean squared error (RMSE)

> Learn how to use the root mean squared error test

## Definition

The root mean squared error (RMSE) test measures the square root of the mean squared error (MSE). RMSE provides a measure of prediction accuracy in the same units as the target variable, making it more interpretable than MSE.

## Taxonomy

* **Task types**: Tabular regression.
* **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

* RMSE is expressed in the same units as the target variable, making it more interpretable than MSE.
* Like MSE, RMSE penalizes larger errors more heavily due to the squaring operation, making it sensitive to outliers.
* Lower RMSE values indicate better model performance, with 0 representing perfect predictions.
* RMSE is widely used in regression tasks and provides a good balance between interpretability and mathematical properties.

## Required columns

To compute this metric, your dataset must contain the following columns:

* **Predictions**: The predicted values from your regression model
* **Ground truths**: The actual/true target values

## Test configuration examples

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

```json Development theme={null}
[
  {
    "name": "RMSE below 10",
    "description": "Ensure that the root mean squared error is below 10",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "measurement": "rmse",
        "operator": "<",
        "value": 10
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]
```

```json Monitoring theme={null}
[
  {
    "name": "RMSE below 10",
    "description": "Ensure that the root mean squared error is below 10",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "measurement": "rmse",
        "operator": "<",
        "value": 10
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]
```

## Related

* [MSE test](https://docs.openlayer.com/tests/catalog/mse.md) - Mean squared error (RMSE squared).
* [MAE test](https://docs.openlayer.com/tests/catalog/mae.md) - Mean absolute error (less sensitive to outliers).
* [R-squared test](https://docs.openlayer.com/tests/catalog/r2.md) - Coefficient of determination.
* [MAPE test](https://docs.openlayer.com/tests/catalog/mape.md) - Mean absolute percentage error.
* [Aggregate metrics](https://docs.openlayer.com/tests/performance/aggregate-metrics) - Overview of all available metrics.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.