The ROC curve is a valuable tool to compare different classifiers that can assign a score to their predictions. In general, this score can be interpreted as a probability, so it's bounded between 0 and 1. The plane is structured as shown in the following diagram:
Standard structure of an ROC plane
The x-axis represents the increasing false positive rate (1 - FPR) also known as 1 - Specificity, defined as follows:
The y-axis represents the true positive rate (TPR) also known as Sensitivity:
The dashed oblique line in the previous ...
