A major shortcoming of Decision Trees is that their results depend on the initiation conditions, however, their reliability can be improved by utilizing many Decision Trees in a RF classifier and averaging the results. In general, this improves accuracy, but reduces the interpretability. To overcome this limitation Decision Rules can be deduced from the results, providing a semantic understanding of the RF classifier. We used 30 Decision Trees, each trained on a subset of the data, to yield the RF. When applied to our dataset, this method provides rules for each of the three classes, as seen in Figure , below. A complete diagram of the Decision Rules can be found in the SI, Figure . Notably, the Decision Rules approach identifies three distinct rule sets for defining a product. Color-coding the PCA with consensus features according to these three clusters as well as k-means clustering shows that three distinct product conformations can be identified, even though the clusters still show minor overlap (see Figure ).