  Predictive models that are developed in a regulated industry or a regulated
application, like determination of credit worthiness, must be interpretable and
rational (e.g., meaningful improvements in basic credit behavior must result in
improved credit worthiness scores). Machine Learning technologies provide very
good performance with minimal analyst intervention, making them well suited to
a high volume analytic environment, but the majority are black box tools that
provide very limited insight or interpretability into key drivers of model
performance or predicted model output values. This paper presents a methodology
that blends one of the most popular predictive statistical modeling methods for
binary classification with a core model enhancement strategy found in machine
learning. The resulting prediction methodology provides solid performance, from
minimal analyst effort, while providing the interpretability and rationality
required in regulated industries, as well as in other environments where
interpretation of model parameters is required (e.g. businesses that require
interpretation of models, to take action on them).
