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SMAPE#

Symmetric Mean Absolute Percentage Error (SMAPE) is a scale-independent regression metric that expresses the relative error of a set of predictions and their labels as a percentage. It is an improvement over the non-symmetric MAPE in that it is both upper and lower bounded.

\[ {\displaystyle {\text{SMAPE}} = {\frac {100\%}{n}}\sum _{t=1}^{n}{\frac {\left|F_{t}-A_{t}\right|}{(|A_{t}|+|F_{t}|)/2}}} \]

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: -100 to 0

Parameters#

This metric does not have any parameters.

Example#

use Rubix\ML\CrossValidation\Metrics\SMAPE;

$metric = new SMAPE();

References#

  1. V. Kreinovich. et al. (2014). How to Estimate Forecasting Quality: A System Motivated Derivation of Symmetric Mean Absolute Percentage Error (SMAPE) and Other Similar Characteristics. ↩

Last update: 2021-03-03