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.
Note: In order to maintain the convention of maximizing validation scores, this metric outputs the negative of the original score.
Estimator Compatibility: Regressor
Output Range: -∞ to 0
use Rubix\ML\CrossValidation\Metrics\MeanAbsoluteError; $metric = new MeanAbsoluteError();