Why is the mean absolute percentage error measure important?

The mean absolute percentage error (MAPE) is significant because it expresses accuracy in percentage terms, making it easier to interpret and compare across different datasets or forecasting models. Being presented as a percentage provides a clear and intuitive understanding of the accuracy of a forecast, allowing stakeholders to gauge performance effectively. This percentage-based approach can be particularly useful when comparing the accuracy of models that might be evaluating different scales or units, as it normalizes the error across different contexts.

In contrast, while other measures provide valuable information—average variances reflect the general deviation of values, linear relationships pertain to correlation rather than accuracy, and total regression effectiveness addresses the overall fit of a model—none offer the same level of straightforward interpretability in percentage form as MAPE does. As a result, MAPE stands out as a practical tool for assessing and communicating forecast accuracy.