[source] # 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 **Score Range:** -100 to 0 ## Parameters This metric does not have any parameters. ## Example ```php 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.