To determine which supervised learning model would be most effective, we used cross validation to systematically explore model hyperparameter combinations in five different model classes. The five model classes were random forest (RF), extreme gradient boosting (XGB), fully connected neural networks (NN), ensembles, and a Similarity Baseline. We did not design new supervised learning methods but rather focused on evaluating well-established algorithms. In this stage, we enumerated hyperparameter combinations, training 3,080 total models, and then pruned the hyperparameter combinations down to the best 20 per model class (Appendix A.7). We split the training dataset into 10 folds and used only folds 0 through 7 in this stage, reserving the last two folds (folds 8 and 9) for the next stage (Table ).