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We present convincing empirical evidence for an effective and generalstrategy for building accurate small models. Such models are attractive forinterpretability and also find use in resource-constrained environments. Thestrategy is to learn the training distribution instead of using data from thetest distribution. The distribution learning algorithm is not a contribution ofthis work; we highlight the broad usefulness of this simple strategy on adiverse set of tasks, and as such these rigorous empirical results are ourcontribution. We apply it to the tasks of (1) building cluster explanationtrees, (2) prototype-based classification, and (3) classification using RandomForests, and show that it improves the accuracy of weak traditional baselinesto the point that they are surprisingly competitive with specialized moderntechniques.  This strategy is also versatile wrt the notion of model size. In the firsttwo tasks, model size is identified by number of leaves in the tree and thenumber of prototypes respectively. In the final task involving Random Foreststhe strategy is shown to be effective even when model size is determined bymore than one factor: number of trees and their maximum depth.  Positive results using multiple datasets are presented that are shown to bestatistically significant. These lead us to conclude that this strategy is botheffective, i.e, leads to significant improvements, and general, i.e., isapplicable to different tasks and model families, and therefore merits furtherattention in domains that require small accurate models.

Data Selection: A Surprisingly Effective and General Principle for Building Small Interpretable Models