AI engineering glossary · Evaluation metrics
What is MAE (mean absolute error)?
Mean absolute error (also called MAE, L1 error): The average size of the prediction errors, ignoring their direction: MAE = (1/N) * sum of |y - y_hat|. It is in the target's own units and treats every unit of error equally, so it is less swayed by outliers than squared-error metrics.
Mean absolute error: an example
For actual [50, 60, 70, 80] and predictions [55, 58, 72, 100], the absolute errors are 5, 2, 2, 20, so MAE = 29 / 4 = 7.25.
Related terms
More Evaluation metrics terms
- Mean absolute percentage error
- Mean absolute scaled error
- Mean average precision
- Mean bias error
- Mean reciprocal rank
- Mean squared error
- Mean squared logarithmic error
- Median absolute error
Where you learn it
Mean absolute error is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.