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  3. Machine Learning with Tree-Based Models in R

Exercise

Variable importance

You already know that bagged trees are an ensemble model that overcomes the variance problem of decision trees. Now you learned that the random forest algorithm further improves this by using only a random subset of the features in each tree. This further decorrelates the ensemble, improving its predictive performance.

In this exercise, you will build a random forest yourself and plot the importance of the predictors using the vip package. The training data, customers_train, is pre-loaded in your workspace.

Instructions

100 XP
  • Create spec, the specification of a random forest classification model using the "ranger" engine and "impurity" variable importance.
  • Create model by fitting the tibble customers_train to spec using still_customer as the outcome and all other columns as the predictor variables.
  • Plot the variable importance using the vip() function from the vip package (which is not pre-loaded).