Bagging is a term for the use of voting and averaging in predictive data mining to synthesize the predictions from many models or methods or the use of the same type of a model on different data.

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Bagging, or Bootstrap Aggregating, involves training multiple versions of a model on different subsets of the data and then combining their predictions through voting (for classification) or averaging (for regression) to improve accuracy and stability.

Bagging Is a Term for the Use of Voting and Averaging