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Bagging or bootstrap aggregation assigns equal weights to each model in the ensemble. It trains each model of the ensemble separately using random subset of training data in order to promote variance. Random Forest is a classical example of bagging technique where multiple random decision trees are combined to achieve high accuracy. Samples are generated in such a manner that the samples are different from each other and replacement is permitted.