Using Random Forest to Learn Imbalanced Data
the Gini criterion for finding splits. In the terminal nodes of each tree, class weights are again taken into consideration. The class prediction of each terminal node is determined by “weighted majority vote”; i.e., the weighted vote of a class is the weight for that class times the number of cases for that class at the terminal node.
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