Chapter 07.06: Discussion
In this section we discuss the advantages and disadvantages of random forests and explain that all advantages of trees also apply here.
Lecture video
Lecture slides
Code demo
Random Forests
You can run the code snippets in the demos on your local machine. The corresponding Rmd version of this demo can be found here. If you want to render the Rmd files to PDF, you need the accompanying style files.
Quiz
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## Which statements are true?
- [x] To compute permutation variable importance for feature $j$, we permute the feature and see how the performance changes (in OOB observations).
- [ ] The random forest is a bad out-of-the box model and requires tuning of hyperparameters.
- [x] Random forests and trees can be used for high-dimensional data.
- [ ] Proximities are used in replacing missing data, but not in locating outliers.