We use our calibrated ABM and our AI algorithm to make case-by-case predictions of outcomes in new out-of-sample test data. These predictions concern: the full partisan composition of the cabinets which form, participation by particular parties in the cabinets which form, and the observed durations of the cabinet which forms. Absent a baseline model of government formation in such complex settings against which we can evaluate our results, we compare success rates with those of a prediction of minimal winning coalitions which is common to a large number of existing studies. Bearing in mind that the ABM in particular generates probability distributions of predicted outcomes in each case, which we feel is substantively realistic, while only a single outcome can be observed, we are very satisfied with the predictive accuracy of the model. Successful predictions relating to cabinet durations are particularly distinctive to the model, deriving from the model-predicted number of issues tabled in formation negotiations, and the model-predicted likelihood than a random shock will create a situation in which a majority of legislators now prefer some alternative to the incumbent.