The accuracy paradox is a phenomenon that occurs in the evaluation of classification models, particularly in imbalanced datasets, where a model may achieve high accuracy despite performing poorly in detecting the minority class. Here's how it works: 1. **Imbalanced Classes**: In many real-world datasets, one class may significantly outnumber another. For example, in a medical diagnosis model for a rare disease, there could be 95% healthy individuals and only 5% who have the disease.
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