Explain how a ROC curve works?
ROC ( Receiver Operating Characteristic ) Curve tells us about how good the model can distinguish between two things ( e.g If a patient has a disease or no ). Better models can accurately distinguish between the two. Whereas, a poor model will have difficulties in distinguishing between the two.
Receiver Operating Characteristic (ROC) curve is a statistical tool utilised to analyse a binary classifier performance. Two objects are classified according to their features. For instance, in the medical field the patients can be classified as having a disease or not by studying the features. The ROC curve is represented on a graph with the True Positive rate on the X axis and the False Positive Rate on the Y axis. The True Positive Rate (TPR) and the False Positive Rate are identified at varied points, and the same is plotted on a graph. The True Positive Rate (TPR) is otherwise called as sensitivity and the False Positive Rate(FPR) is otherwise called as specificity.