AI engineering glossary · Evaluation metrics
What is accuracy paradox?
Accuracy paradox: The situation where a model with high accuracy is useless because the classes are heavily imbalanced. A model that always predicts the common class scores well on accuracy while never finding the rare class the task is about. It is why precision, recall and related metrics are preferred for imbalanced problems.
Accuracy paradox: an example
If only 1% of 10,000 loan applicants default, predicting 'no default' for everyone gives 99% accuracy but catches zero defaulters.
Related terms
More Evaluation metrics terms
Where you learn it
Accuracy paradox is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.