Mean Absolute Error (MAE)
A regression loss function that averages the absolute differences between predictions and true values, offering greater robustness.
Mean Absolute Error measures prediction error in a more direct way by averaging absolute differences between predicted and true values. Unlike MSE, it does not square the errors, which makes it less sensitive to outliers. This often makes MAE more robust and easier to interpret. It is especially useful in problems where we want to assess error size in a linear way. However, its optimization behavior can differ because the derivative structure is less smooth at certain points. Choosing between MAE and MSE is therefore often problem-dependent.
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