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. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 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.
• 2 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.
• 3 However, this comparison can present a false dichotomy.

This work deepens our understanding of the fundamental laws governing the universe, from subatomic particles to cosmic structures.

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