8.14 Advantages/Disadvantages of decision trees

• Trees can be displayed graphically and are very easy to explain to people

• They mirror human decision-making

• Can handle qualitative predictors without the need for dummy variables

BUT,

• They do not have the same level of predictive accuracy

• Can be very non-robust (i.e., a small change in the data can cause large change in the final estimated tree)

• To improve performance, we can use an ensemble method, which combines many simple ‘buidling blocks’ (i.e., regression or classification trees) to obtain a single and potentially very powerful model

• ensemble methods include: bagging, random forests, boosting, and Bayesian additive regression trees