AI engineering glossary · Evaluation metrics
What is weighted averaging?
Weighted averaging (also called weighted-average, weighted F1): A way to combine per-class metrics by averaging them with weights proportional to how many true examples each class has. It reflects the class distribution, so a weak rare class has little effect on the final number, unlike macro averaging.
Weighted averaging: an example
Class A (900 samples) has F1 0.95 and class B (100 samples) has F1 0.50; the weighted F1 is 0.9 * 0.95 + 0.1 * 0.50 = 0.905.
Related terms
More Evaluation metrics terms
- Weighted kappa
- Word error rate
- Youden's J statistic
- Accuracy
- Accuracy paradox
- Adjusted Rand index
- AUC-ROC
- Average precision
Where you learn it
Weighted 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.