AI engineering glossary · Evaluation metrics
What is MAPE (mean absolute percentage error)?
Mean absolute percentage error (also called MAPE): The average of the absolute errors expressed as a percentage of the true values: (100/N) * sum of |y - y_hat| / |y|. It is easy to communicate but breaks down when true values are zero or near zero, and it penalises over-forecasts more than under-forecasts.
Mean absolute percentage error: an example
For actuals 50, 60, 70, 80 and predictions 55, 58, 72, 100 the percentage errors are 10%, 3.3%, 2.9% and 25%, giving MAPE of about 10.3%.
Related terms
- Symmetric mean absolute percentage error
- Mean absolute error
- Mean absolute scaled error
- Root mean squared error
More Evaluation metrics terms
- Mean absolute scaled error
- Mean average precision
- Mean bias error
- Mean reciprocal rank
- Mean squared error
- Mean squared logarithmic error
- Median absolute error
- METEOR
Where you learn it
Mean absolute percentage 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.