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#
use Rubix\ML\CrossValidation\Metrics\MeanAbsoluteError;
$metric = new MeanAbsoluteError();