Ensemble Learning and Random Forests

Estimated time: 1.5 hours

Read Chapter 7: Ensemble Learning and Random Forests, from the beginning of the chapter up to, but not including, Boosting.

In the course edition, this corresponds approximately to pp. 211–222.

This reading introduces the broader idea of ensemble learning before focusing on random forests. The central idea is that combining multiple models can produce a more reliable predictor than relying on a single model.

Focus especially on:

• the idea of ensemble learning;
• how multiple decision trees can be combined;
• why random forests are more stable than individual decision trees;
• the important hyperparameters of a random forest.

First Pass: Get the Big Picture — 10 minutes

• Skim the section titles.
• Look at the plots and figures.
• Read exercise questions 1–5.

Second Pass: Read for Understanding — 60 minutes

Read from the beginning of Chapter 7 until the Boosting section.

Focus on understanding why combining multiple models works and how random forests introduce randomness across trees.

Third Pass: Review — 20 minutes

Answer exercise questions 1–5.

Use the chapter to check and refine your answers.