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Exercise

Sifting through variable importance

The attrition dataset contains 839 observations and 30 predictors for "Attrition." You are interested in exploring the trade-off between the performance of a model that uses all available predictors versus a reduced model based on a few informative variables.

In this exercise, you'll fit a model and have a look at the variable importance of this fitted model. In the following exercise, you'll assess model performance using this model compared to using a reduced model.

The train and test splits and the vip() package are available in your environment along with a predeclared logistic regression model.

Instructions

100 XP
  • Create a recipe that models Attrition using all predictors.
  • Fit the workflow to the training data.
  • Use the fit_full object to graph the variable importance of your model.
  • Apply the extract_fit_parsnip() function before vip() to feed it the required information.