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. 15 min read, ~2,306 words.

Single tree: low bias, high variance, unstable to small data perturbations. Ensembles attack one of those two failure modes. Bagging: averages bootstrap-trained trees; ensemble variance is, so the floor persists even as grows. Assumes trees roughly unbiased. Random forest: bagging plus a random -predictor subset per split ( classification, regression)...

What this lesson covers

• Content
• Example 1
• Example 2
• Common Mistakes
• Check Your Understanding
• Exam Shortcuts

Learning objectives

• C4

Browse all free MAS-II lessons or jump into free MAS-II practice questions.