When evaluating the performance of an ML model, it is important to use a withheld test set from the original dataset. Since the true values for this test set are known, they serve as a reference against which the predicted values can be compared. For TS geometry prediction, the predicted geometry can be compared to its true reference geometry using two common metrics: Distance Mean Absolute Error (D-MAE) and Root Mean Square Deviation (RMSD).