title: Balanced Random Forest in scikit-learn (python)
body: <p>I'm wondering if there is an implementation of the Balanced Random Forest (BRF) in recent versions of the scikit-learn package. BRF is used in the case of imbalanced data. It works as normal RF, but for each bootstrapping iteration, it balances the prevalence class by undersampling. For example, given two classes N0 = 100, and N1 = 30 instances, at each random sampling it draws (with replacement) 30 instances from the first class and the same amount of instances from the second class, i.e. it trains a tree on a balanced data set. For more information <a href="http://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf" rel="noreferrer">please refer to this paper</a>.</p>
  
  <p>RandomForestClassifier() does have the 'class_weight=' parameter, which might be set to 'balanced', but I'm not sure that it is related to downsampling of the bootsrapped training samples.</p>