Mean Absolute Percentage Error for regression models

By Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi. Neurocomputing, 2016

1,317 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b976a5738538ea8aed7396628fc0a3ad17b30787

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In Management Science and Operations Research

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Citations in the corpus

Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization (2021, background): “• Mean Absolute Percentage Error (MAPE): as given in [17], MAPE is the most common measure used to predict errors, and works perfectly if there are no extreme values in the data.”
Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error (2017, neutral): “This approach is useful when the size or size of a prediction variable is significant in evaluating the accuracy of a prediction[7][8].”
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation (2021, neutral): “…another performance metric for regression models, having a very intuitive interpretation in terms of relative error: due to its de ﬁ nition, its use is recommended in tasks where it is more important being sensitive to relative variations than to absolute variations ( De Myttenaere et al., 2016 ).”
Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Other common evaluation metrics were the coefficient of correlation (R), coefficient of determination (R2; basically, the square of the correlation coefficient), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), which can be given via the following relat”
Extraction of mechanical properties of materials through deep learning from instrumented indentation (2020, methodology): “Throughout our work, the level of accuracy in estimating any mechanical property is quantified by the MAPE (47).”