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The Ethereal
Using the Mean Absolute Percentage Error for Regression Models
June 12, 2015 ยท Declared Dead ยท ๐ The European Symposium on Artificial Neural Networks
"No code URL or promise found in abstract"
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Authors
Arnaud De Myttenaere, Boris Golden, Bรฉnรฉdicte Le Grand, Fabrice Rossi
arXiv ID
1506.04176
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.LG
Citations
44
Venue
The European Symposium on Artificial Neural Networks
Last Checked
6 months ago
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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