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Ensemble learning methods like Bagging and Boosting that train a diversity of classifiers by manipulating the training set given to a base learning algorithm and then combine their predictions in classification. And the trade-off between accuracy and diversity of component classifiers is the crucial factor in determining its generalization error. Both Bagging and Boosting approach the trade-off by...
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