Figure clearly illustrates that by oversampling the less frequent reactions, we see a significant increase in the accuracy for the less used templates. The top-1 accuracy for reactions that occur less than or equal to five times increases from 33.8% to 50-54%, depending on how the test set sampling is performed. However, the way that the model behaves for frequent reactions is quite different. The model where each template is represented by exactly 30 reactions (grey line) shows a dramatic decrease in accuracy for frequently used templates. Also, the average template occurrence for the model is only 78, which suggests that the model is heavily biased towards less frequent reactions. The strong bias indicates that we have thrown too much information away due to a very aggressive undersampling of common templates. The two other models, that oversample the rare reactions (cyan and yellow lines), both clearly exhibit a more horizontal trend. The horizontal trend indicates that the accuracy are more balanced across all templates than the model trained on the unprocessed training set. Another way to illustrate this is by comparing the average top-1 template occurrence which is 1492 and 1389 for the slightly oversampled (cut-off 10, cyan line) and the "heavily" oversampled (cutoff of 50, yellow line), respectively. These average template occurrences are much better in line with the true values of the test set, albeit there is still room for improvement. The slightly over oversampled model performs worse on less frequently used templates then the "heavily" oversampled model. However, around the a template of 100 occurrence the "heavily" oversampled model falls below the model trained on the standard training set. The drop below the standard model means that even though the accuracy for rare reactions is better the overall accuracy of the model is a bit worse. The applicability of the templates recommended by the slightly and "heavily" oversampled models are pretty similar. As seen in Table , both models, on average, needs the top-28 templates to reach a cumulative probability of 0.995 (or maximally top-50). But among the recommended templates, only 10 and 11 of them are applicable. Figure show the average cumulative probability of the models. It illustrates that the increase in cumulative probability is more significant when fewer templates are included for the oversampled graph methods. It confirms the tendency that increased model confidence will result in fewer templates needed to reach the cumulative probability of 0.995 (or maximally 50 templates). Consequently, it will recommend fewer applicable templates. Compared to the ECFP recommender model, the two oversampled models will find 5 and 6 fewer applicable templates from the USPTO test set.