Skip to main content

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.

​
Definition

The mean absolute error (MAE) test measures the average of the absolute differences between the predicted values and the true values. MAE provides a linear measure of prediction accuracy that is less sensitive to outliers compared to MSE and RMSE.

​
Taxonomy

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

​
Why it matters

  • MAE is expressed in the same units as the target variable, making it highly interpretable.
  • Unlike MSE and RMSE, MAE treats all errors equally regardless of their magnitude, making it more robust to outliers.
  • Lower MAE values indicate better model performance, with 0 representing perfect predictions.
  • MAE provides a straightforward measure of average prediction error that is easy to understand and communicate.

​
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 MAE test: 
[
  {
    "name": "MAE below 5",
    "description": "Ensure that the mean absolute error is below 5",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "mae",
        "operator": "<",
        "value": 5
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]