Building and plotting rpart() models

It was noted in the video that decision trees represent a popular form of predictive model because they are easy to visualize and explain. It was also noted that the rpart package is probably the most popular of several R packages that can be used to build and visualize these models.

This exercise asks you to, first, build a decision tree model using the rpart() function from this package, and then display the resulting model structure using the generic functions plot() and text().

Load the rpart package to make the rpart() modeling function and the associated methods for generic functions like plot() available.
Use the rpart() function to fit a decision tree model tree_model that predicts medv in the Boston data frame from all of the other variables in this data frame.
Apply the plot() function to tree_model to obtain an unlabelled plot of the structure of this decision tree model.
Apply the text() function to tree_model to label this plot. To make the labels easier to read, use the cex parameter to reduce the text to 70% of the default size.

Take Hint (-30 XP)

Exercise

Building and plotting rpart() models

Instructions