Using the 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 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.
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Communication dans un congrès
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015)
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Contributeur : Fabrice Rossi <>
Soumis le : jeudi 11 juin 2015 - 18:07:29
Dernière modification le : samedi 13 juin 2015 - 01:05:40
Document(s) archivé(s) le : samedi 12 septembre 2015 - 11:11:08

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  • HAL Id : hal-01162980, version 1
  • ARXIV : 1506.04176

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Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi. Using the Mean Absolute Percentage Error for Regression Models. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015). <hal-01162980>

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