subsampling, which is also known as Monte Carlo crossvalidation
as multiple holdout or as repeated evaluation set ,
is based on randomly splitting the data into subsets, whereby the size of the
subsets is defined by the user .
The random partitioning of the data can be repeated arbitrarily often. In contrast
to a full crossvalidation procedure, random subsampling has been shown to be
asymptotically consistent  resulting in more pessimistic
predictions of the test data compared with crossvalidation. The predictions
of the test data give a realistic estimation of the predictions of external
validation data .