title: Dealing with unbalanced datasets in Spark MLlib
body: <p>I'm working on a particular binary classification problem with a highly unbalanced dataset, and I was wondering if anyone has tried to implement specific techniques for dealing with unbalanced datasets (such as <a href="https://www.jair.org/media/953/live-953-2037-jair.pdf" rel="noreferrer">SMOTE</a>) in classification problems using Spark's MLlib.</p>
  
  <p>I'm using MLLib's Random Forest implementation and already tried the simplest approach of randomly undersampling the larger class but it didn't work as well as I expected.</p>
  
  <p>I would appreciate any feedback regarding your experience with similar issues.</p>
  
  <p>Thanks,</p>