Subsemble: an ensemble method for combining subset-specific algorithm fits.
Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive datasets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset 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 datasets, and often has better prediction performance than the underlying algorithm fit just once on the full dataset. We also describe how to include Subsemble as a candidate in a SuperLearner library, providing a practical way to evaluate the performance of Subsemlbe relative to the underlying algorithm fit just once on the full dataset.
Department of Statistics, University of California at Berkeley, Berkeley, CA, USA.
Stephanie
S
Sapp
Division of Biostatistics, University of California at Berkeley, Berkeley, CA, USA.
Mark J
MJ
van der Laan
Division of Computer Science, University of California at Berkeley, Berkeley, CA, USA.
John
J
Canny
big data; cross-validation; ensemble methods; machine learning; prediction