![]() The figure above shows some example ROC curves. “0”), the AUROC tells you the probability that a randomly selected “1” image will have a higher predicted probability of being a “1” than a randomly selected “0” imageĪUROC is thus a performance metric for “discrimination”: it tells you about the model’s ability to discriminate between cases (positive examples) and non-cases (negative examples.) An AUROC of 0.8 means that the model has good discriminatory ability: 80% of the time, the model will correctly assign a higher absolute risk to a randomly selected patient with an event than to a randomly selected patient without an event.įigure: ROC Curves (modified from this cartoon) ![]() For a binary handwritten digit classification model (“1” vs.For a clinical risk prediction model, the AUROC tells you the probability that a randomly selected patient who experienced an event will have a higher predicted risk score than a randomly selected patient who did not experience an event ( ref). ![]() AUROC tells you whether your model is able to correctly rank examples: The area under the receiver operating characteristic (AUROC) is a performance metric that you can use to evaluate classification models.
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