We are interested in ensembles of models built over k data sets. Common approaches are either to combine models by vote averaging, or to build a meta-model on the outputs of the local models. In this paper, we consider the model assignment approach, in which a meta-model selects one of the local statistical models for scoring. We introduce an algorithm called Greedy Data Labeling (GDL) that improves the initial data partition by reallocating some data, so that when each model is built on its local data subset, the resulting hierarchical system has minimal error. We present evidence that model assignment may in certain situations be more natural than traditional ensemble learning, and if enhanced by GDL, it often outperforms traditional ensembles.