Tree-based Methods

Advantages and Disadvantages

  • Tree-based methods are simple and useful for interpretation

  • However they typically are not competitive with the best supervised learning approaches in terms of prediction accuracy

  • Hence we also discuss bagging, random forests, and boosting. These methods grow multiple trees which are then combined to yield a single consensus prediction

  • Combining a large number of trees can often result in dramatic improvements in prediction accuracy, at the expense of some loss interpretation

The Basics of Decision Trees

Terminology for Trees

Interpretation of Results

Details of the Tree-Building Process