Mean absolute percentage error (MAPE) is expressed as what?

Mean Absolute Percentage Error (MAPE) is a metric used to assess the accuracy of a forecasting method. It expresses the prediction error as a percentage of the actual values, allowing for a straightforward interpretation of the error relative to the size of the values being forecasted.

The correct choice, which indicates that MAPE is the average of absolute percentage errors, highlights that this metric involves taking the absolute difference between the actual and forecast values, dividing by the actual values, and then averaging these results over the dataset. This average helps to understand how far the forecasts deviate from the actual results as a percentage, making it especially useful when comparing the accuracy of different forecasting models across different scales.

The other options do not accurately describe MAPE:

• The average squared errors refers to a different measure known as Mean Squared Error (MSE), which does not reflect percentage errors at all.

• The standard deviation of the errors provides information about the variability of the errors, but it does not quantify accuracy in percentage terms like MAPE does.

• The actual value divided by forecast value would not yield a meaningful metric in itself for assessing accuracy; it could lead to misunderstandings without further contextualization.

Thus, recognizing that MAPE specifically focuses on averages of absolute percentage errors