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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.

This exercise is part of the course

Machine Learning with Tree-Based Models in R

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Exercise instructions

  • 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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Predict on training set and add to training set
predictions <- ___(___,
                   new_data = ___, 
                   type = "___") %>% 
    bind_cols(___)

# Create and plot the ROC curve
roc_curve(___,
          estimate = ___,
          truth = ___) %>% autoplot()

# Calculate the AUC
___(predictions,
    estimate = ___, 
    truth = ___)
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