AI engineering glossary · Evaluation metrics
What is MASE (mean absolute scaled error)?
Mean absolute scaled error (also called MASE): A scale-free forecasting metric that divides the model's mean absolute error by the mean absolute error of a simple naive forecast on the training data, such as predicting the last observed value. A value below 1 means the model beats the naive method. It works when series have different scales.
Mean absolute scaled error: an example
A model with MAE 4 on a series where the naive forecast has MAE 5 gets MASE = 0.8.
Related terms
- Mean absolute error
- Mean absolute percentage error
- Baseline model
- Symmetric mean absolute percentage error
More Evaluation metrics terms
- Mean average precision
- Mean bias error
- Mean reciprocal rank
- Mean squared error
- Mean squared logarithmic error
- Median absolute error
- METEOR
- Micro averaging
Where you learn it
Mean absolute scaled 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.