Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not
Geoscientific Model Development
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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, Willmott and Matsuura (2005) and Chai and Draxler (2014) 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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not |
| Series title | Geoscientific Model Development |
| DOI | 10.5194/gmd-15-5481-2022 |
| Volume | 15 |
| Year Published | 2022 |
| Language | English |
| Publisher | European Geosciences Union |
| Contributing office(s) | Central Midwest Water Science Center |
| Description | 7 p. |
| First page | 5481 |
| Last page | 5487 |
| Google Analytic Metrics | Metrics page |