Using Random Forest to Learn Imbalanced Data

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training data. In learning extremely imbalanced data, there is a significant probability that a bootstrap sample contains few or even none of the minority class, resulting in a tree with poor performance for predicting the minority class. A na¨ıve way of fixing this problem is to use a stratified bootstrap; i.e., sample with 2

  Data, Forest, Random, Imbalanced data, Imbalanced, Random forests