Verified65% confidenceFactTime unknown
AUC equals the probability that, when one positive sample and one negative sample are randomly drawn from all positive and negative samples respectively, the predicted score of the positive sample is higher than that of the negative sample.
3
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65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
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UnverifiedAUC的概率意义是随机抽取一个正例和一个负例，模型对正例打分高于负例的概率，该特性使其对类别不平衡不敏感82% similarVerifiedAUC值域为0.5到1.0，0.5代表随机猜测、1.0代表完美预测，不受样本类别不平衡影响79% similarUnverifiedCalibration means that when the model outputs 0.9, across a large number of similar samples about 90% are indeed the correct class; an uncalibrated model can have high AUC but its values cannot be used directly as probabilities.72% similarUnverified在8个模型均随机猜测的假设下，单次二元预测中至少有一个押中的概率约99.6%72% similarUnverifiedword2vec负采样中，对每个正样本随机采样k个（通常5-20个）负样本，采样概率通常按词频的3/4次方分布71% similar
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