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Add to EdgeUsing the Mean Absolute Percentage Error for Regression Models
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Jun 12, 2015
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.
* 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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