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Due to decay, the AUC of total counts is not an AUC of concentration.
An AUC of 1.0 indicates perfect classifier whereas an AUC of classifier no better than random is 0.5.
An AUC of 1.0 indicates perfect concordance, whereas an AUC of 0.5 indicates no relationship.
ROC analysis revealed an AUC of 0.90.
The HFA risk stratification tool had an AUC of 0.54.
A combined clinical model predicted survival with AUC of 0.78.
The predictive performance showed an AUC of 0.74 [0.66–0.82].
An AuC of 0.5 indicates a random classifier; an AuC of 1 indicates a perfect classifier.
The model is considered good with an AUC of 0.867.
Model quality is good with an AUC of 0.899.
A perfect test would have an AUC of 1; a test with no discriminatory power, an AUC of 0.5.
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