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Classification and Regression by randomForest

Classification and Regression by randomForest

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ging, successive trees do not depend on earlier trees — each is independently constructed using a boot-strap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of …

  Ging

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