Using Random Forest to Learn Imbalanced Data

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sensitive, and it penalizes misclassifying the minority class. The other combines the sampling technique and the ensemble idea. It down-samples the majority class and grows each tree on a more balanced data set. A majority vote is taken as usual for prediction. We compared the prediction performance with one-

  Forest, Random, Misclassifying, Random forests