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ensemble learning
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Bagging ensemble algorithm description: bagging is an ensemble learning method that trains multiple different weak learners into a strong learner. Bagging is a parallel training process. Multiple subsamples of classification test are obtained by putting back samples of classification test samples, T base classifiers are trained by classification sub samples. When each instance is classified, the T base classifiers are called respectively to get t results. Finally, the test instance is given to the class with the most frequent occurrence in the T classification results.
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