AI engineering glossary · Evaluation metrics
What is macro averaging?
Macro averaging (also called macro-average, macro F1): A way to combine per-class metrics in multiclass problems by computing the metric separately for each class and taking a plain average, so every class counts equally regardless of size. It exposes poor performance on rare classes that micro averaging would hide.
Macro averaging: an example
Three classes have recall 0.9, 0.9 and 0.3. The macro-averaged recall is 0.7, even if the weak class is tiny.
Related terms
More Evaluation metrics terms
- Matthews correlation coefficient
- MAUVE
- Max error
- Maximum calibration error
- McNemar's test
- Mean absolute error
- Mean absolute percentage error
- Mean absolute scaled error
Where you learn it
Macro averaging is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.