  A methodology that seeks to enhance model prediction performance is
presented. The method involves generating multiple auxiliary models that
capture relationships between attributes as a function of each other. Such
information serves to generate additional informative columns in the dataset
that can potentially enhance target prediction. A proof of case and related
code is provided.
