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When should one use mean absolute error over root mean square error as a performance measure for regression problems?
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Answer / Vijay Kumar Kannojia
Mean Absolute Error (MAE) is more appropriate than Root Mean Square Error (RMSE) when dealing with datasets having outliers or when the errors are asymmetric. MAE is less sensitive to large errors and can provide a clearer picture of model performance in such cases.
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