Interpretable machine learning techniques have proven invaluable in dealing with the vast amount of data, because of their ability to trace results back to structural changes. However, a consensus model is needed to identify the internal coordinates with the highest contribution to separating educt, TS and product, and to eliminate the inherent variability and instability of individual ML approaches.