As an additional perspective, leave-one-out cross-validation (LOO-CV) , is performed for each final optimization dataset and visualized via parity plots (Figure ). This is a technique where one datapoint is held out from a model for evaluating performance and the model is trained on the rest of the datapoints, and the process is repeated for every datapoint. For more information on LOO-CV, see . The average LOO-CV mean absolute error (MAE) is computed at the end of each optimization campaign and is the model accuracy referred to in Section 3.1.