In real-world environments it is usually di cult to specify target operating conditions precisely. This uncertainty makes building robust classi cation systems problematic. We show that it is possible to build a hybrid classi er that will perform at least as well as the best available classi er for any target conditions. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. In some cases, the performance of the hybrid can actually surpass that of the best known classi er. The hybrid is also e cient to build, to store, and to update. Finally, we provide empirical evidence that a robust hybrid classi er is needed for many real-world problems.