Exercise

Interpreting decision tree

Great job on the multivariate regression model! Now, try meeting your client's need for an interpretable ML solution with a regression tree to predict the value of the fuel consumption (consume) variable based on all other features.

The car dataset and the test_instance variable you worked with in the previous exercise are available again in your workspace. Additionally, the rpart, rpart.plot and rattle packages have been pre-loaded. You will need them to create a regression tree, visualize it and extract decision rules from the tree, respectively.

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Build a regression tree using rpart() to predict consume using all other predictors in the car dataset. Name your model car_dt.