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Bagging and boosting both fuse many weak learners — like shallow decision trees — into one strong predictor, but along opposite axes. Bagging trains trees in parallel on random bootstrap samples and averages them, so their roughly independent errors cancel out and variance drops. Boosting trains trees one after another, each new one ...
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