Train a regression tree model

In this exercise, we will use the grade_train dataset to fit a regression tree using rpart() and visualize it using rpart.plot(). A regression tree plot looks identical to a classification tree plot, with the exception that there will be numeric values in the leaf nodes instead of predicted classes.

This is very similar to what we did previously in Chapter 1. When fitting a classification tree, we use method = "class", however, when fitting a regression tree, we need to set method = "anova". By default, the rpart() function will make an intelligent guess as to what the method value should be based on the data type of your response column, but it's recommened that you explictly set the method for reproducibility reasons (since the auto-guesser may change in the future).

The grade_train training set is loaded into the workspace.

Using the grade_train dataframe and the given formula, train a regresion tree.
Look at the model output by printing the model object.
Plot the decision tree using rpart.plot().

Exercise

Train a regression tree model

Instructions