Exercise

In-sample ROC and AUC

How well do bagged trees capture the structure of your training set? Are they better than decision trees? Do they overfit? Using ROC and AUC is a great way of assessing this.

In this exercise, you are going to generate these in-sample predictions and calculate their ROC and AUC. Listen up, there will be surprises!

Pre-loaded is the result of the previous exercise, model_bagged, and training data, customers_train.

Instructions

100 XP

Use model_bagged to generate probability predictions with your training set and add them to the training set tibble, saving the result as predictions.
Generate the ROC curve of the predictions tibble and plot the result.
Calculate the AUC of the predictions tibble.