Training data is generated by a combination of Latin Hypercube sampling (LHS) of the input phase space as well as a guided search of high quality, Pareto-optimal points through a bootstrap optimisation technique. The data obtained using these sampling techniques is then cleaned and pre-processed by applying log-like transformations to each of the KPIs. Next, the data is partitioned into train / test / validation datasets using a ratio of