Afterward, the relative importance of the molecular properties (features) -the three DMA multipole and the number of nitro groups, most relevant for predicting the target feature ℎ .# in each case, according to each algorithm, was investigated. For this step, the feature_importances tool was used to estimate the importance of each property for a given algorithm during its training step. For the four algorithms, SHAP (Shapley Additive exPlanations) plots 105 were built to display how much each feature (independent variable used in the regression analysis) -in our case, a molecular property, contributed either positively or negatively to the predicted ℎ .# value with respect to the average value of the set. This is a game theoretical approach developed to rationalize the output of any ML model, and it was used recently in a neural network QSPR work on impact sensitivity. Those plots can be drawn for a given molecule or collectively for the complete test set.