The results from the major anomer predicting models indicate that these models give a high accuracy prediction and are somewhat able to generalize. Therefore, we wanted to understand what was going on "under the hood". Here, we chose to take a closer look at the decision tree models, as interpretation of these models is more straightforward than of the more highly parameterized models. The goals of this investigation were to (1) understand the hierarchy of the rules dictating the stereochemical outcome of glycosylations and (2) determine if models rediscovered expert glycosylation rules and/or identified new rules interpretably by experts. First, we started by comparing a simplified DT model with the expert decision trees introduced earlier. For this comparison, the depth of the decision tree model was restricted to 4 to be comparable to the expert trees which have a depth of 3 and 4. All three trees and their accuracy when evaluated on the whole O-glycosylation dataset can be found in Figure . The decision tree algorithm, despite being restricted, still outperforms the experts in accuracy. It should be noted that the decision tree algorithm was asked to predict the earlier defined Mills configuration (i.e. -1 or 1), rather than α/β, like the experts. Thus, some of the improvement in accuracy compared to the experts might stem from this change in the description of the stereochemistry at the anomeric center. For ease of comparison, the most prevalent α/β-anomer in each leaf is also indicated.