It is often difficult to interpret the output of ML models because the methods provide very little information on the descriptors' contribution to the output. We employed Shapley additive explanation 75 (SHAP) to test whether our chemical intuition agrees with the results of the ML model. Feature importance analysis is also performed. Additionally, Matplotlib, Seaborn, and Pandas 78 as the main Python visualization and data manipulation libraries were used. The SI provides more details on the package dependencies as well as a suitable environment for ML calculations.