When investigating variable importance, two techniques come to mind as versatile and broadly applicable: random forests and multi-linear regressions. Both have yielded impressive results in chemistry and other fields, however, for HiTEA we chose to utilize random forests . Unlike multi-linear regression, random forests do not stipulate that one's data must be linear, and thus obviate the need for linearization (and ideally normalization). Given the non-linearity of the data, we hypothesized that random forests would yield more accurate variable importances. In general, moderate to good out of bag accuracy of reaction outcome from a random forest with standard hyperparameters was observed (Table ), with some noted exceptions (see Taking Dataset to HiTEA sections), correlating with poorer mechanistic insights of the reaction class overall. To assess the confidence of the variable importance, ANOVA was performed on each dataset subclass with statistical significance of the variables set at p = 0.05.