AI engineering glossary · Evaluation metrics
What is error analysis?
Error analysis: The practice of manually inspecting a model's mistakes, grouping them by type or cause, and counting each group, in order to decide what to fix next. It turns an aggregate score into concrete actions such as collecting more data for one case or fixing a labelling problem.
Error analysis: an example
Reviewing 100 wrong predictions of an invoice reader shows 40 come from blurry scans, so improving image quality is prioritised over changing the model.
Related terms
More Evaluation metrics terms
- Exact match
- Expected calibration error
- Expected reciprocal rank
- Explained variance score
- F-beta score
- F1 score
- False negative
- False negative rate
Where you learn it
Error analysis is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.