There are several ways in which our ensembling approach could be improved. In a typical stacking ensemble, 76 the ensemble is trained on predictions that the base models did not train on. If the training set is denoted as A, then one common stacking method is to do K-fold cross validation with the base models and then use the stacked K held-out fold predictions to train the ensemble. Another strategy is to split A into two subsets, one for training the base models, and one for generating base model predictions to train the ensemble. However, we train the base models on a dataset A, generate predictions on A, and train the ensemble on these predictions. This can introduce a bias in a parameter-based ensemble like Model-Based Ensemble towards favoring base models that have a better fit on A. The Max Vote