Area Under the ROC Curve

The Area Under the Receiver Operating Characteristic Curve (ROC AUC) is a measure of the likelihood that the model will rank a randomly chosen positive example higher than a randomly chosen negative example. Another way of putting it is to say that the higher this measure is, the better the model is at predicting a negative class as negative, and a positive class as positive. The value ranges from 0 to 1. If the AUC is 0.6, it means that the model has a 60% probability of correctly distinguishing a negative class from a positive class based on the inputs. This measure is used to compare models.

Exercise 6.13: Computing the ROC AUC for the Caesarian Dataset

The goal of this exercise is to compute the ROC AUC for the binary classification model that you trained in Exercise 6.12, Computing and Plotting ROC Curve for a Binary Classification Problem.

Note

You should continue this exercise in the same notebook as that used in Exercise 6.12,...