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 atests.json, here are a few valid configurations for the MAE test:
Related
- MSE test - Mean squared error (more sensitive to outliers).
- RMSE test - Root mean squared error (square root of MSE).
- R-squared test - Coefficient of determination.
- MAPE test - Mean absolute percentage error.
- Aggregate metrics - Overview of all available metrics.