Definition
The mean squared error (MSE) test measures the average of the squared differences between the predicted values and the true values. MSE provides a measure of how close predictions are to the actual outcomes, with larger errors being penalized more heavily due to the squaring operation.Taxonomy
- Task types: Tabular regression.
- Availability: and .
Why it matters
- MSE is one of the most commonly used metrics for evaluating regression model performance.
- The squaring of errors means that larger prediction errors are penalized more heavily than smaller ones, making MSE sensitive to outliers.
- Lower MSE values indicate better model performance, with 0 representing perfect predictions.
- MSE is differentiable, making it suitable for gradient-based optimization algorithms during model training.
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 MSE test:
Related
- RMSE test - Root mean squared error (square root of MSE).
- 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.