Apply bagging and random forests as appropriate.
Free SOA Exam PA (Predictive Analytics) lesson in Tree-Based Models. 11 min read, ~1,654 words.
Bagging is bootstrap aggregating: fit a tree on each of B bootstrap resamples, then average predictions (regression) or majority-vote (classification). Bagging reduces variance, not bias, so grow deep unpruned trees as the base learners. Random forests improve bagging by sampling only mtry predictors at each split, which decorrelates the trees...
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What this lesson covers
- Content
- Example 1
- Example 2
- Common Mistakes
- Check Your Understanding
- Exam Shortcuts
Learning objectives
- 5b
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