Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not
Abstract The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, and give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not ; volume:15 ; number:14 ; year:2022 ; pages:5481-5487 ; extent:7
Geoscientific model development ; 15, Heft 14 (2022), 5481-5487 (gesamt 7)
- Creator
-
Hodson, Timothy O.
- DOI
-
10.5194/gmd-15-5481-2022
- URN
-
urn:nbn:de:101:1-2022072105164636940143
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:37 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hodson, Timothy O.
Other Objects (12)
Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
The proximal map of the weighted mean absolute error
The proximal map of the weighted mean absolute error
Beauchamp; or, The error
Scalar mean square error optimal linear combination of multivariate forecasts
Scalar Mean Square Error Optimal Linear Combination of Multivariate Forecasts
Best possible inequalities for the harmonic mean of error function
Colloquium: Quantum root-mean-square error and measurement uncertainty relations
Mean Markets or Kind Commerce?
Relative mean error and standard deviation in dependency on the throughput
Optimum design of chamfer masks using symmetric mean absolute percentage error
Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
The proximal map of the weighted mean absolute error
The proximal map of the weighted mean absolute error
Beauchamp; or, The error
Scalar mean square error optimal linear combination of multivariate forecasts
Scalar Mean Square Error Optimal Linear Combination of Multivariate Forecasts
Best possible inequalities for the harmonic mean of error function
Colloquium: Quantum root-mean-square error and measurement uncertainty relations
Mean Markets or Kind Commerce?
Relative mean error and standard deviation in dependency on the throughput
Optimum design of chamfer masks using symmetric mean absolute percentage error
Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
The proximal map of the weighted mean absolute error
The proximal map of the weighted mean absolute error
Beauchamp; or, The error
Scalar mean square error optimal linear combination of multivariate forecasts
Scalar Mean Square Error Optimal Linear Combination of Multivariate Forecasts
Best possible inequalities for the harmonic mean of error function
Colloquium: Quantum root-mean-square error and measurement uncertainty relations
Mean Markets or Kind Commerce?
Relative mean error and standard deviation in dependency on the throughput