The training data is also trained using sklearn's DummyRegressor and the mean average error (MAE) is compared alongside that of DiSCoVeR's to serve as a metric. During testing, the NOMAD data set is partitioned into chunks to help with computation. The predictions for high-performing compositions are appended and organized after being screened through the trained model.