Next, we turned our attention to comparing the performance of the best GNN-based model with other non-graph-based machine learning models with a particular emphasis on more interpretable methods. Here we chose random forests (RFs) and simple decision tree (DT) models, as the single-tree nature of these models lends them to interpretation. We wanted to investigate if the models could serve as a useful tool for carbohydrate chemists and we therefore chose to benchmark against human experts. Two carbohydrate chemists with over 20 years of experience in the field were asked to create an anomeric selectivity decision tree using their chemical knowledge. The trees were then encoded as algorithms and applied to the dataset (the algorithms can be found on GitHub; trees are depicted in Figure and discussed below). To see how the size of the dataset influences the predictive power of the models, two learning curves were created (Figure ). Two different splits were investigated, a random split (Figure ) and a publication split (Figure ), mimicking two different application scenarios: predicting the major product of well-known procedures and predicting the major product of novel glycosylation methods (assuming most new publications contain some novelty in the glycosylation procedure). The RF models generally give the best performance, and all models outperform the experts when trained on >1500 glycosylation reactions. A minor loss in performance is observed when the glycosylation dataset is split according to publication. However, the models still outperform both experts despite the more difficult nature of this split. Noticeably, the DT-based models show comparable performance to the best models, allowing a meaningful in-depth analysis of these, which will be discussed later.