A way to achieve this is to do a splitting with "filters" mode, and define a filter by a formula.
For example, split into "train_set_with_more_churners" and "test_set_with_fewer_churners", use:
* A filter that sends into "train_set_with_more_churners" with formula like:
if (churner == 1, rand() < 0.8, rand() < 0.5)
* Send all other values into "test_set_with_fewer_churners"
* 80% of churners will be sent to train set, 20% of churners to test set
* 50% of non-churners will be sent to train set, 50% to test set
If you have enough data and can afford to waste some, you can also use a sampling recipe in "class rebalancing" mode (but that will subsample so you will remove some non-churners)