Verified70% confidenceFactTime unknown
When samples are severely imbalanced, Accuracy becomes misleading—a model that predicts everyone as healthy can still achieve an accuracy of over 99%.
4
Sources
70%
Confidence
Long-term
Relevance
6/1/2026
First Seen
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Related Claims
UnverifiedCalibration does not equal high accuracy; a system that reports 95% confidence on everything may still be overconfident or blindly optimistic even if its accuracy reaches 95%.79% similarUnverifiedAverage accuracy masks distributional skew; a judge that gets all easy cases right and is overconfident on hard cases can post a high average score while failing at critical points.77% similarUnverified'99% 准确率'的模型在生产环境中仍可能频繁出错，因为剩余的 1% 往往对应现实中最关键的长尾细节场景77% similarUnverifiedIn classification tasks with imbalanced classes, accuracy can be misleading; for example, if only 20 out of 1000 tickets are truly urgent, a model that predicts 'not urgent' for all of them can still achieve 98% accuracy.77% similarUnverifiedIn class-imbalance scenarios, a model can achieve seemingly impressive accuracy by always predicting the majority class, a phenomenon known as the Accuracy Paradox.77% similar
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