The Shapley Value of Classifiers in Ensemble Games

Understand

What is the value of an individual model in an ensemble of binary classifiers? We answer this question by introducing a class of transferable utility cooperative games called \textit{ensemble games}.

• In machine learning ensembles, pre-trained models cooperate to make classification decisions.
• To quantify the importance of models in these ensemble games, we define \textit{Troupe} -- an efficient algorithm which allocates payoffs based on approximate Shapley values of the classifiers.
• We argue that the Shapley value of models in these games is an effective decision metric for choosing a high performing subset of models from the ensemble.