Subsemble: an ensemble method for combining subset-specific algorithm fits

Stephanie Sapp, Mark J. van der Laan and John Canny

Journal of Applied Statistics, 2014, vol. 41, issue 6, 1247-1259

Abstract: Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive data sets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large data sets. Subsemble partitions the full data set into subsets of observations, fits a specified underlying algorithm on each subset, and uses a clever form of V -fold cross-validation to output a prediction function that combines the subset-specific fits. We give an oracle result that provides a theoretical performance guarantee for Subsemble. Through simulations, we demonstrate that Subsemble can be a beneficial tool for small- to moderate-sized data sets, and often has better prediction performance than the underlying algorithm fit just once on the full data set. We also describe how to include Subsemble as a candidate in a SuperLearner library, providing a practical way to evaluate the performance of Subsemble relative to the underlying algorithm fit just once on the full data set.

Date: 2014
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Citations: View citations in EconPapers (1)

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DOI: 10.1080/02664763.2013.864263

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