Empirical risk minimization is consistent with the mean absolute percentage error

2015/09/08 by De Myttenaere, Arnaud, Grand, Bénédicte Le, Rossi, Fabrice
#FOS: Computer and information sciences #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1509.02357

Abstract

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 also show that, under some asumptions, universal consistency of Empirical Risk Minimization remains possible using the MAPE.

Related

• Using the Mean Absolute Percentage Error for Regression Models
• On the Minimal Error of Empirical Risk Minimization
• On the Variance, Admissibility, and Stability of Empirical Risk Minimization
• Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not
• A Fundamental Accuracy--Robustness Trade-off in Regression and Classification