A supervised learning algorithm inputs a train set,
and outputs a prediction function, which can be used on a test set.
If each data point belongs to a subset
(such as geographic region, year, etc), then
how do we know if subsets are similar enough so that
we can get accurate predictions on one subset,
after training on Other subsets?
And how do we know if training on All subsets would improve
prediction accuracy, relative to training on the Same subset?
SOAK, Same/Other/All K-fold cross-validation,