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Exercise

Make custom train/test indices

As you saw in the video, for this chapter you will focus on a real-world dataset that brings together all of the concepts discussed in the previous chapters.

The churn dataset contains data on a variety of telecom customers and the modeling challenge is to predict which customers will cancel their service (or churn).

In this chapter, you will be exploring two different types of predictive models: glmnet and rf, so the first order of business is to create a reusable trainControl object you can use to reliably compare them.

Instructions
100 XP

churn_x and churn_y are loaded in your workspace.

  • Use createFolds() to create 5 CV folds on churn_y, your target variable for this exercise.
  • Pass them to trainControl() to create a reusable trainControl for comparing models.
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