Mean Absolute Percentage Error for regression models - Archive ouverte HAL Access content directly
Journal Articles Neurocomputing Year : 2016

Mean Absolute Percentage Error for regression models

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 prove the existence of an optimal MAPE model and we show the universal consistency of Empirical Risk Minimization based on the MAPE. We also show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression, and we apply this weighting strategy to kernel regression. The behavior of the MAPE kernel regression is illustrated on simulated data.
Fichier principal
Vignette du fichier
demyttenaeregoldenetal2016mean-absolute.pdf (538.82 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01312590 , version 1 (07-05-2016)
hal-01312590 , version 2 (10-07-2017)

Licence

Attribution - ShareAlike

Identifiers

Cite

Arnaud de Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi. Mean Absolute Percentage Error for regression models. Neurocomputing, 2016, Advances in artificial neural networks, machine learning and computational intelligence — Selected papers from the 23rd European Symposium on Artificial Neural Networks (ESANN 2015), 192, pp.38 - 48. ⟨10.1016/j.neucom.2015.12.114⟩. ⟨hal-01312590v2⟩
280 View
4874 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More