Unverified60% confidenceBenchmarkExact time
In that heart disease prediction experiment, the logistic regression model achieved an AUC of 0.89.
2
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60%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
UnverifiedAUC is a comprehensive evaluation metric for binary classification models, with a value range of 0.5 to 1.0; it is not affected by class imbalance, and in clinical prediction, an AUC of 0.7–0.8 has some reference value, 0.8–0.9 is considered good, and above 0.9 is considered excellent.69% 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.69% similarVerifiedIn a well-calibrated model, an output score of 0.8 should statistically correspond to an actual accuracy of 80%.68% similarUnverifiedJudge disagreement predicts errors, with AUROC up to 0.80, but the variance inflation from inverse probability weighting exceeds the signal gain.67% similarUnverifiedOn the training samples, the character classification model achieved a macro-averaged AUC of 0.97 and a Cohen's κ of 0.81.67% similar
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