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Bagging Predictors

Why this mattered

TBD

Abstract

(no abstract available)

  • cite → Classification and Regression Trees. — Bagging Predictors uses classification and regression trees as unstable base learners whose variance can be reduced by bootstrap aggregation.
  • enables → Random Forests — Bagging introduced bootstrap aggregation of unstable predictors, which random forests combined with random feature selection across decision trees.
  • cite ← Random Forests — Random Forests extends bagging by training many bootstrap decision trees while adding random feature selection at each split.
  • enables ← Classification and Regression Trees. — CART supplied the high-variance decision-tree learners that bagging stabilized through bootstrap aggregation.

Sources