Machine Learning with R, the tidyverse, and mlr

Chapter 8. Improving decision trees with random forests and boosting

This chapter covers

• Understanding ensemble methods
• Using bagging, boosting, and stacking
• Using the random forest and XGBoost algorithms
• Benchmarking multiple algorithms against the same task

In the last chapter, I showed you how we can use the recursive partitioning algorithm to train decision trees that are very interpretable. We finished by highlighting an important limitation of decision trees: they have a tendency to overfit the training set. This results in models that generalize poorly to new data. As a result, individual decision trees are rarely used, but they can become extremely powerful predictors when many trees are combined together.

By the end of this chapter, ...