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Documentation Index

Fetch the complete documentation index at: https://openlayer.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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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.

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Taxonomy

  • Task types: Tabular regression.
  • Availability: and .

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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.

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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

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Test configuration examples

If you are writing a tests.json, here are a few valid configurations for the RMSE test: 
[
  {
    "name": "RMSE below 10",
    "description": "Ensure that the root mean squared error is below 10",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "rmse",
        "operator": "<",
        "value": 10
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]