Unverified50% confidenceFactExact time
When evaluating a binary classification model, the majority-class baseline when positive and negative samples are evenly split means a model that always predicts 'normal' can also achieve 50% accuracy.
1
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50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Claims
Unverified一个随机猜测的模型在单次二元预测中有50%的概率押中76% similarUnverifiedDuring testing, cases emerged where a model claimed 95% confidence in a move but scored 0 points according to the evaluation criteria.74% similarUnverifiedA perfectly calibrated model should fall on the 45-degree diagonal in a reliability diagram, whereas an overconfident model's curve systematically lies below the diagonal.71% similarUnverifiedUnder the condition of n=100 with an even split of positive and negative samples, the 95% confidence interval for 55% accuracy is roughly 45%–65%, which covers the 50% baseline, so the 5 percentage point gap may be random fluctuation.70% similarUnverifiedThe gap between selection and generation exceeds 50 percentage points, revealing that reliability is far lower than benchmarks suggest when users need the model to directly write out kinship terms.70% similar
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