Presented in Figure are the ROC AUC and F1 scores as evaluated on the test-set for RF, SVC, and XGB models trained with different hyperparameter search strategies. We used a 75:25 train:test split. This data is presented for the phase stability classifier trained on the full experimental data for each sub-system. Here, RFs are the best-performing model across each sub-system. However, Tables show that this is not always the case. An SVC or XGB may also be the best performing model at different iterations of the DoE. Figure shows the ML performance differs in an unpredictable way based on the hyperparameter search strategy. We cannot pick a single best method and therefore, we exhaustively try all the tools at our disposal to ultimately develop the most predictive models, whose ROC curves are shown in Figure .