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FRM Part I · FRM Exam Part I · Machine Learning and Prediction

Which statement correctly distinguishes a random forest from plain bagging of decision trees?

A random forest considers only a random subset of predictors at each split, in addition to bootstrapping the data. This decorrelates the trees so that averaging them reduces variance more than plain bagging, which allows every predictor at every split.

  1. AA random forest uses boosting so each tree corrects the errors of the previous tree
  2. BA random forest considers only a random subset of predictors at each split, which decorrelates the treesCorrect
  3. CA random forest trains every tree on the full original sample without resampling
  4. DA random forest prunes all trees to a single split before averaging

Explanation

Both methods use bootstrap samples, but random forests also restrict each split to a random subset of features. This lowers correlation among trees, so averaging reduces variance more. Boosting is sequential and is a different ensemble approach.

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