Definition
The R-squared test measures the coefficient of determination, which quantifies the proportion of the variance in the dependent variable that is predictable from the independent variables. R-squared indicates how well the regression model explains the variability of the target variable.Taxonomy
- Task types: Tabular regression.
- Availability: and .
Why it matters
- R-squared provides an intuitive measure of model performance as a percentage of variance explained.
- Values range from 0 to 1 (or negative for very poor models), where 1 indicates perfect prediction and 0 indicates the model performs no better than predicting the mean.
- Higher R-squared values indicate better model performance and stronger explanatory power.
- R-squared is scale-independent, making it useful for comparing models across different datasets and target variables.
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 R-squared test:
Related
- MSE test - Mean squared error.
- RMSE test - Root mean squared error.
- MAE test - Mean absolute error.
- MAPE test - Mean absolute percentage error.
- Aggregate metrics - Overview of all available metrics.