MCDMError metric
Mean Absolute Error (MAE)
Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.
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Sources
- Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat. link ↗
- Brossier, C. L. (1999). Consistency of trimmed and Winsorized L-estimators of location and scale. Journal of the American Statistical Association, 74(368), 813-821. DOI: 10.1080/01621459.1979.10481033 ↗
- Huber, P. J. (2009). Robust Statistics (2nd ed.). Hoboken, NJ: John Wiley & Sons. ISBN: 978-0470129906