While the previous approaches identified the relevant internal degrees of freedom which determine the reaction coordinate, as a next step, we sought to quantify it, that is, to define ranges which separate the data into the three ensembles. A method to achieve this, is the Decision Tree, which splits ensembles by setting cut-offs based on the coordinates that show the largest distribution differences between classes. The Decision Tree in Figure has been trained on the whole dataset. By balanced weighting given to the groups via oversampling, biasing against the transition state ensemble, which contains significantly fewer structures, is removed. For comparison, the results of an unbalanced tree can be found in Figure .