A false positive occurs when a model incorrectly predicts a positive outcome for a negative instance. This misclassification is particularly critical in contexts where the cost of falsely identifying a condition can lead to unnecessary anxiety or treatment. Understanding false positives is essential for evaluating models, as it directly impacts metrics like precision and recall, influencing the overall performance assessment.
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