  We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We show that
finding the best model under the MAPE is equivalent to doing weighted Mean
Absolute Error (MAE) regression. We show that universal consistency of
Empirical Risk Minimization remains possible using the MAPE instead of the MAE.
