Before revision:  The players in ensemble games are pre-trained binary classifiers that collaborate in an ensemble to correctly label points from a dataset. We design Troupe a scalable algorithm that designates payoffs to individual models based on the Shapley value of those in the ensemble game. We show that the approximate  Shapley value of classifiers in these games is an adequate measure for selecting a subgroup of highly predictive models .
After revision:  The players in ensemble games are pre-trained models cooperate to make classification decisions. To quantify the importance of models in these ensemble games, we define Shapley value of classifiers in these games is an adequate measure for selecting a subgroup of highly predictive models .