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.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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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)
- Urheber
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Hodson, Timothy O.
- DOI
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10.5194/gmd-15-5481-2022
- URN
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urn:nbn:de:101:1-2022072105164636940143
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:37 MESZ
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Beteiligte
- Hodson, Timothy O.
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