ROC

Receiver Operating Characteristic

A graphical representation to evaluate the performance of a binary classification model. Binary classification models predict one of two possible outcomes, such as whether an advertisement will likely be clicked or a customer will likely convert.

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 the model correctly identifies. In contrast, the false positive rate (FPR) is the proportion of actual negative cases incorrectly identified as positive.

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

• Abbreviation: ROC
• Source: What is Artificial Intelligence