  A graph-based protocol called `learning networks' which combine assorted
machine learning models into meta-models is described. Learning networks are
shown to overcome several limitations of model composition as implemented in
the dominant machine learning platforms. After illustrating the protocol in
simple examples, a concise syntax for specifying a learning network,
implemented in the MLJ framework, is presented. Using the syntax, it is shown
that learning networks are are sufficiently flexible to include Wolpert's model
stacking, with out-of-sample predictions for the base learners.
