Understand the assumptions underlying different tree ensemble methods and the improvements they can make to decision trees.
Free CAS MAS-II (Modern Actuarial Statistics II) lesson in Statistical Learning. 13 min read, ~1,973 words.
Single tree: low bias, high variance, unstable to small data perturbations. Ensembles attack one of those two failure modes. Bagging: reduces variance by averaging bootstrap-trained trees. Assumes trees are roughly unbiased and that bootstrap injects enough independence. Random forest: bagging plus a random subset of predictors at each split (...
Read the full lesson, free →
Worked examples, audio narration, and practice. No signup to read.
What this lesson covers
- Content
- Example 1
- Example 2
- Common Mistakes
- Key Takeaways
- Exam Shortcuts
Learning objectives
- C4
Browse all free MAS-II lessons or jump into free MAS-II practice questions.