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ROC curves and diagnostic thresholds

Understand the model, recognise it in a stem, separate the look-alikes, then apply it.

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Visual explanationUse the diagram to rebuild the concept from memory.
1.0 0 0 1.0 chance (AUC 0.5) best cut-off (top-left) 1 − specificity (false positives) sensitivity
ROC curve
Moving the cut-off trades sens vs spec high cut-off: high spec, low sens low cut-off: high sens, low spec 1 − specificity sensitivity lower the threshold → catch more cases, more false positives
Moving the cut-off
01Memory anchor
Top-left is best; diagonal is chance; lower the cut-off and rises as falls.

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