Mean Absolute Error (MAE) measures average absolute differences between predicted and actual values, offering a robust scale-independent error metric less affected by outliers than squared errors.

Mean Absolute Error (MAE) provides a straightforward absolute measure of prediction accuracy, averaging the absolute differences between predicted and actual values without squaring. This makes MAE less sensitive to outliers than squared metrics, offering a robust evaluation for datasets with anomalies. MAE highlights the average magnitude of errors in the target's units, with lower values indicating precise fits.

The formula for MAE is:

\text{MAE} = \frac{1}{n} \sum_{i=1}^n |y_i - \hat{y}_i|

Here, $y_i$ represents actuals, $\hat{y}_i$ predictions, and $n$ the sample size, yielding an error in the variable's units.

MAE's interpretability shines in practical applications where average error directly translates to impact. Compare it with RMSE to balance outlier sensitivity.

A limitation is its lack of relative scaling, so pair with percentage metrics for comprehensive views. MAE is ideal for median-based optimizations.