💡 What's the difference between bagging and boosting?

Bagging and boosting are both ensemble methods, meaning they combine many weak predictors to create a strong predictor.

One key difference is that bagging builds independent models in parallel and "averages" their results in the end, whereas boosting builds models sequentially, at each step emphasizing reducing error that remains in the model by better fitting to the observations that were missed in previous steps.

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