Like F1 and recall, the ROC curve focuses only on the positive class and doesn’t show how well your model does on the negative class. Davis and Goadrich suggested that we should plot precision against recall instead, in what they termed the Precision-Recall Curve. They argued that this curve gives a more informative picture of an algorithm’s performance on tasks with heavy class imbalance.
