mlsanityData to 7 Oct 2026

Mean Absolute Percentage Error for regression models

arXiv 1605.02541 ↗First posted 9 May 2016stat.MLcited by 58

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Abstract (arXiv)

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.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 May 2016.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 11; 2024: 10; 2025: 12; 2026: 8.

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