). These results are computed based on the decrease in model performance if a particular feature is removed. Another popular method in the field of ML interpretability is the use of SHAP (SHapley Additive exPlanations) . These values show how each feature affects the final prediction . SHAP is based on the magnitude of feature attributions. Feature importances and SHAP values are different measures, but it is interesting to note that the order of features presented in both Figures 7a and 7b are very similar. In both cases, the concentration of thickener is the most important factor governing phase stability, with Figure showing less thickener is better for preparing stable formulations. These results are directly interpretable for the chemist or formulator, as we have attributed the stability (or instability) to surfactant functional groups, or polymer and thickener concentrations. Furthermore, as shown for an illustrated sample in Figure , SHAP can also provide feature attributions on a sample-by-sample basis for a deeper investigation of a formulation's properties. These a posteriori analyses can aid in developing novel formulated products. Further discussion linking the chemistry of the functional groups to the phase stability results is out of the scope of this work and will be treated in a future discussion.