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