We consider the top 20 (with ties) hyperparameter sets from the hyperparameter tuning stage for each base model class based on mean performance of NEF 1% . This gives a total of 103 selected models (the 3 additional models are included due to ties). We train all of the candidate models (103 base models, 1 Similarity Baseline, and 26 ensembles) a single time. We use folds 0 through 7 for training, fold 8 for validation, and fold 9 for testing. This yields a total of 130 performance measures on AUC[ROC], AP, and NEF 1% . The single top performing model is selected for the two prospective screening stages. In the RF-C model class, three models were tied in NEF 1% . To break the tie, we used AP to select the best RF-C model.