  We present a new methodology for utilising machine learning technology in
symbolic computation research. We explain how a well known human-designed
heuristic to make the choice of variable ordering in cylindrical algebraic
decomposition may be represented as a constrained neural network. This allows
us to then use machine learning methods to further optimise the heuristic,
leading to new networks of similar size, representing new heuristics of similar
complexity as the original human-designed one. We present this as a form of
ante-hoc explainability for use in computer algebra development.
