### IR Accuracy Accuracy measures the overall correctness of the model and is the ratio of the correctly identified predictions (both true positives and true negatives) to the total number of cases examined. For content extraction, it reflects how often the model was correct, both in identifying relevant content and in ignoring non-relevant content. However, accuracy can be misleading in the context of imbalanced classes, where one category dominates over another. **Formula:** Accuracy=True Positives+True NegativesTotal Number of Examples\text{Accuracy} = \frac{\text{True Positives} + \text{True Negatives}}{\text{Total Number of Examples}}Accuracy=Total Number of ExamplesTrue Positives+True Negatives​