One way to get a diverse set of classifiers is to use very different training algorithms,as just discussed. Another approach is to use the same training algorithm for everypredictor but to train them on different random subsets of the training set. Whensampling is performed with replacement, this method is called bagging(short for bootstrap aggregating). When sampling is performed without...
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