MAE or the Mean Absolute Error is an average of the absolute errors. The smaller the MAE the better the model’s performance. The MAE units are the same units as your data’s dependent variable/target (so if that’s dollars, this is in dollars), which is useful for understanding whether the size of the error is meaningful or not. MAE is not sensitive to outliers. If your data has a lot of outliers, then examine the Root Mean Square Error (RMSE), which is sensitive to outliers.
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