AI engineering glossary · Evaluation metrics
What is symmetric mean absolute percentage error?
Symmetric mean absolute percentage error (also called sMAPE): A variant of the percentage error that divides each absolute error by the average of the actual and predicted magnitudes: (100/N) * sum of |y - y_hat| / ((|y| + |y_hat|) / 2). It softens the over-forecast bias of MAPE and is used in forecasting competitions, though it still misbehaves near zero.
Symmetric mean absolute percentage error: an example
For actuals 50, 60, 70, 80 and predictions 55, 58, 72, 100, sMAPE is about 9.5%.
Related terms
More Evaluation metrics terms
- Temperature scaling
- Token-level F1
- Translation edit rate
- True negative
- True positive
- V-measure
- Weighted averaging
- Weighted kappa
Where you learn it
Symmetric 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.