To evaluate the performance of a ML model, the dataset is partitioned into training and testing data. A model is trained (using the training data), before being given the inputs for the, previously unseen, testing data and asked to make predictions on the outputs. The difference between the predictions and the actual values is given with the root mean squared error (RMSE), a common performance metric. To partition the dataset into training and test sets, we applied two different strategies, a random split, and a designed split. When data for the training and testing partitions are chosen at random it is very likely that the training data contains information that is well distributed amongst the dataset and, as such, is in part representative of the test data. Thus, a random split may be considered as an interpolative prediction task. To simulate out-of-sample prediction the training/test partitions can be chosen with the intention of neglecting a specific part of the dataset, for example by excluding one ligand from the training data via a leave-one group out (LOGO) cross validation (CV). After the model has been trained, it is given the testing data that was poorly represented in the training data and attempts to make predictions. In this way a train/test partition can be designed so as to simulate an extrapolative prediction of unseen data. To obtain more general results, many different train-test partitions are used and an average RMSE of the predictions based on the test dataset is calculated and used as a performance metric.