[source] # Mean Absolute Error A scale-dependent metric that measures the average absolute error between a set of predictions and their ground-truth labels. One of the nice properties of MAE is that it has the same units of measurement as the labels being estimated. $$ {\displaystyle \mathrm {MAE} = {\frac {1}{n}}{\sum _{i=1}^{n}\left |Y_{i}-\hat {Y_{i}}\right|}} $$ !!! note In order to maintain the convention of *maximizing* validation scores, this metric outputs the negative of the original score. **Estimator Compatibility:** Regressor **Score Range:** -∞ to 0 ## Parameters This metric does not have any parameters. ## Example ```php use Rubix\ML\CrossValidation\Metrics\MeanAbsoluteError; $metric = new MeanAbsoluteError(); ```