We consider the problem of configuring classifier trees in distributed stream mining system. The configuration involves selecting appropriate false-alarm detection tradeoffs for each classifier to minimize end-to-end penalty in terms of mis-classification cost. We model this as a tree configuration game and design solutions, where individual classifiers select their operating points to maximize a local utility. We derive appropriate misclassification cost coefficients for intermediate classifiers, and determine the information that needs to be exchanged across classifiers, in order to successfully design the game. We analytically show that there is a unique pure strategy Nash equilibrium in operating points, which guarantees a convergence of the proposed approach. We evaluate the performance of our algorithm on an application for sports scene classification, and compare against centralized solutions. We show that our algorithm results in better performance than the centralized solution on average. Moreover, the algorithm approaches the optimal solution asymptotically with increasing number of actions per classifier.