Data Selection: A General Principle for Building Small Interpretable Models
This provides a general strategy for improving small models, which is incremental as it applies existing distribution learning to enhance accuracy in specific domains.
The paper tackles the problem of building accurate small models for interpretability and resource-constrained environments by learning the training distribution and sampling data accordingly, showing improvements that make traditional baselines competitive with modern techniques across tasks like cluster explanation trees, prototype-based classification, and Random Forests.
We present convincing empirical evidence for an effective and general strategy for building accurate small models. Such models are attractive for interpretability and also find use in resource-constrained environments. The strategy is to learn the training distribution and sample accordingly from the provided training data. The distribution learning algorithm is not a contribution of this work; our contribution is a rigorous demonstration of the broad utility of this strategy in various practical settings. We apply it to the tasks of (1) building cluster explanation trees, (2) prototype-based classification, and (3) classification using Random Forests, and show that it improves the accuracy of decades-old weak traditional baselines to be competitive with specialized modern techniques. This strategy is also versatile wrt the notion of model size. In the first two tasks, model size is considered to be number of leaves in the tree and the number of prototypes respectively. In the final task involving Random Forests, the strategy is shown to be effective even when model size comprises of more than one factor: number of trees and their maximum depth. Positive results using multiple datasets are presented that are shown to be statistically significant.