To evaluate the predictive performance of the models, the data was divided into a training and test set, according to run rather than aggregated samples. Due to the small number of runs within the dataset, the test set consisted of a single run and all datapoints from a run were kept together within the same partition to prevent leakage from the training set into the test set which would give an overly optimistic prediction. Uniform Manifold Approximation and Projection (UMAP) was used in conjunction with K-means clustering algorithm to visualise and group similar runs together.