We intended to further improve the separation of the three states in the PCA by utilizing the labelling of the data, indicating each structure as educt, product or transition state. Using this information, we trained a model that maximized the separation between the three ensembles and simultaneously reduced the number of internal coordinates (features) to those that contribute the most to the separation. The latter is referred to as feature elimination. There are various ways of achieving this goal and we tested a selection of them in PCA and Linear Discriminant Analysis (LDA) dimensionality reduction approaches (see Figure ). While PCAs depend on the technique used to eliminate the features (Figure -C and SI Figure ), LDA is independent of the feature selection and consistently yields excellent separation (Figure and SI Figure . While LDA results in compact distinct ensembles, PCA also achieves separation within the ensembles depending on the feature elimination approach that was used. Analysis of the PCA loadings (see SI Table ) revealed that amongst the top contributors to separating the three ensembles are the change in the distances defining the reaction centre (C-N, Phe-N, NPh-N, NPh-C and Phena-Cu-Br). Secondary features such as the distance between the copper and O7, belonging to the phenanthroline-cage connection, also plays an important role for the Logistic Regression (LR) classifier (Figure ). The Random Forest (RF) identifies the distances defining the reaction centre as being important, as well as the distance between the aniline and phenanthroline, along the second principal component (Figure ). While in RF PC1 has the highest loading of all methods (0.68, see Table ), the separation between the states is best for the LassoCV approach. However, the there is only one ensemble visible for the product state (Figure ). When analysing the LDA loadings, unlike PCA, the first principal component in LDA accounts for 99.9% of the dataset's variance (see SI Table ). Main contributing features are the changes in the distances around the catalytic centre and cage participants are dominating, with Cu-C, Cu-Phe and Phe-Phena (Figure ).