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Sensitivity & specificity are descriptive statistics that measure the quality of a classification system. They are the proportion of real positives that are classified correctly (sensitivity) & the proportion of real negatives that are classified correctly (specificity). They are an integral part of ROC analysis.

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Is it possible for a model to have higher sensitivity/specificity but lower accuracy and AUC?

In the evaluation of classification models, I've found one model to have a higher accuracy and c-statistic (AUC) as compared to a second model. However, the second model has higher sensitivity, specif …
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