Computer scienceData scienceMachine learningClassificationClassification metrics

ROC and AUC

The ROC-AUC overview

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What are the general interpretations of the ROC-AUC?

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An AUC of 0.5 indicates that the model is akin to random guessing

The main application of ROC-AUC is binary classification models

ROC-AUC gives a visual representation of the model's prediction quality

Lower AUC is better than higher AUC

An AUC of 1 indicates the perfect classification model

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