title: Unbalanced data and cross-validation
body: <p>I would like to know if there is an automatic way to deal with unbalanced data. Let's say it is a binary classification problem, and I want to use cross-validation, for example kfolds.</p>
  
  <p>Do you know if there is any cross-validation technique that does not only randomly shuffle the data, but also tries to "balance" the labels in the train set so it is 50%-50%? That would help to not get high accuracy scores, just because the model has higher changes to learn more points from a particular class.</p>