 multiple subsets of a data set as the test set and using the other data as training. This can be done by partitioning the data and using each subset in turn as the test set with the remaining subsets as training data. A partition into ten subsets is common to reduce computational overhead. In the limit, when the test set is just a single item, the result is known as leave-one-out (LOO) cross-validation <span class="citation" data-cites="VehtariEtAl:2017">(<a href="#ref-VehtariEtAl:2017" role="doc-biblioref