Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not

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.

Automated summary

The root-mean-squared error (RMSE) and mean absolute error (MAE) are commonly used metrics for evaluating models, but there is confusion regarding their use and no single metric is inherently better in all cases.