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- W2002718637 abstract "AbstractEnsemble 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.Keywords: ensemble methodspredictioncross-validationmachine learningbig data AcknowledgementsThis work was supported by the National Science Foundation [Graduate Research Fellowship], and the National Institutes of Health [R01 AI074345-06]." @default.
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- W2002718637 date "2013-12-03" @default.
- W2002718637 modified "2023-10-18" @default.
- W2002718637 title "Subsemble: an ensemble method for combining subset-specific algorithm fits" @default.
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- W2002718637 doi "https://doi.org/10.1080/02664763.2013.864263" @default.
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