We tried to investigate the underlying reasons of why there is a difference in both model performance and impact of learning strategy between the data sets. Firstly, we can observe that the Suzuki dataset has five discrete parameters that varied in the combinatorial space as opposed to the four for Buchwald-Hartwig. The extra parameter leads to additional interactive effects from a model building perspective. Secondly, the dataset was more skewed towards the "successful" label, but there was not a severe imbalance between the two labels. The dependency on each feature of the dataset with respect to the labels was investigated in the following way. For each reaction in the datasets, we fixed all features except one and computed the frequency that the label would remain the same while varying the nonfixed feature. We repeated this frequency computation across all features in both datasets. This yields a number between 0.5 and 1, where a frequency of 1 represents all reactions having the same label when a given feature was changed, implying that the feature would be completely interchangeable and uninformative for the classification task. The frequency 0.5 represents the case when all reactions have an even 50% split of labels. There is not a linear relationship between lower frequency and more information. These mean frequencies across each feature are presented in Table .