title: Split imbalanced (binary) data set so test set has equal number of false and positive samples
body: <p>I am trying to apply a NN to a binary classification problem with high class imbalance; 28k False (0.) and 68k True (1.). Is it possible to use stratify with train_test_split to ensure the proportion of True and False events is the same in the test set, but not the train set? For example; Test set - 14k False, 14k true. Train set - 14k False, 54k True.</p>