Receiver Operating Characteristic Area Under Curve (Roc-Auc)
ROC-AUC, or Receiver Operating Characteristic Area Under Curve, is a performance measurement for classification problems in machine learning. The ROC curve is a graphical representation that illustrates the performance of a binary classifier model at varying threshold values. It plots the true positive rate (TPR) against the false positive rate (FPR) at different classification thresholds.
Areas of application
- Healthcare
- Finance
- Marketing
- Security
Example
For instance, let’s say we have a machine learning model that is used to classify emails as spam or not spam. We can plot the ROC curve for this model by varying the threshold for classifying an email as spam. The resulting curve will show us the trade-off between the true positive rate (TPR) and the false positive rate (FPR) at different thresholds.