Receiver Operating Characteristic

ROC is the acronym for Receiver Operating Characteristic.

A graphical representation used to evaluate the performance of a binary classification model. Binary classification models are those that predict one of two possible outcomes, such as whether an email is spam or not spam.

The ROC curve is a plot that illustrates the true positive rate (sensitivity) against the false positive rate (1-specificity) for different classification thresholds. The true positive rate (TPR) is the proportion of actual positive cases that are correctly identified by the model, while the false positive rate (FPR) is the proportion of actual negative cases that are incorrectly identified as positive.

The area under the ROC curve (AUC-ROC) provides a single metric to summarize the model’s performance across all thresholds. A model with perfect classification ability would have an AUC-ROC of 1, while a model that performs no better than random chance would have an AUC-ROC of 0.5. A higher AUC-ROC value indicates better model performance.

  • Abbreviation: ROC

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