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FRM Part I · FRM Exam Part I · Machine-Learning Methods

Which statement best distinguishes a random forest from simple bagging of decision trees?

A random forest randomly selects a subset of features at each split, in addition to bootstrapping observations. This decorrelates the trees, so averaging reduces variance more than in plain bagging. Sequential error correction is boosting, not a random forest.

  1. AA random forest uses only a single bootstrap sample for all trees
  2. BA random forest considers only a random subset of features at each split, which lowers correlation among treesCorrect
  3. CA random forest builds trees sequentially, each correcting the previous tree's errors
  4. DA random forest requires all features to be considered at every split

Explanation

Both methods train trees on bootstrap samples, but a random forest also restricts each split to a random subset of predictors. This decorrelates the trees and makes averaging more effective at reducing variance. Sequential error correction describes boosting.

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