Utilizing the labelled data in PCA and LDA combined with supervised ML approaches resulted in a much better separation of the three ensembles. While the performance of PCA was highly dependent on the internal coordinate set, LDA showed remarkable separation between the three ensembles, highlighting the robustness of the method. A consensus model developed to combine the performance of the various dimensionality and feature reduction methods, identified 49 internal coordinates to be relevant, with the C-N distance being the most prominent one, which is in agreement with chemical intuition.