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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.

\[ {\displaystyle \mathrm {MAE} = {\frac {1}{n}}{\sum _{i=1}^{n}\left |Y_{i}-\hat {Y_{i}}\right|}} \]


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


This metric does not have any parameters.


use Rubix\ML\CrossValidation\Metrics\MeanAbsoluteError;

$metric = new MeanAbsoluteError();