# Using the Mean Absolute Percentage Error for Regression Models

arXiv: https://arxiv.org/abs/1506.04176 · HTML: https://mlsanity.com/paper/1506.04176/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2015.

## Built on by (most cited first, 2 of 2)

- [Exploring Deep Learning Approaches to Predict Person and Vehicle Trips: An Analysis of NHTS Data](https://mlsanity.com/paper/2308.05665/) (2023)
- [Residual-based Adaptive Huber Loss (RAHL) -- Design of an improved Huber loss for CQI prediction in 5G networks](https://mlsanity.com/paper/2408.14718/) (2024)

## 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 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.