title: R H20 - Cross-validation with stratified sampling and non i.i.d. rows
body: <p>I'm using H2O to analyse a dataset but I'm not sure how to correctly perform cross-validation on my dataset. I have an unbalanced dataset, so I would like to performed stratified cross-validation ( were the output variable is used to balance the groups on each partition). </p>
  
  <p>However, on top of that, I also have an issue that many of my rows are repeats (a way of implementing weights without actually having weights). Independently of the source of this problem, I have seen before that, in some cases, you can do cross-validation were some rows have to be kept together. This seams to be the usage of fold_column. However, it is not possible to do both at the same time?</p>
  
  <p>If there is no H2O solution, how can I compute the fold a priori and use it on H2O?</p>