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Mean Absolute Error is a performance metric applied to a regression problem and it is a measure of errors between predicted outcomes versus correct, true outcomes. For any couple (predicted outcome, true value of the outcome) the error is the difference between the two elements, i.e. predicted outcome – true value of the outcome. MAE is computed as the sum of absolute errors divided by the sample size.