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AI & Machine Learning · Intermediate

Bagging

Bootstrap aggregating: training models in parallel on random samples with replacement and averaging their outputs.

Definition

Each model sees a different bootstrap sample, so individual errors partially cancel when combined. Bagging mainly reduces variance and is most helpful for high-variance learners such as deep decision trees. Random forest is the best-known bagging method.

Exam tip

Bagging trains in parallel; boosting trains sequentially.

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