The Subsemble algorithm is 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 unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. The paper, "Subsemble: An ensemble method for combining subset-specific algorithm fits" is authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014) <doi:10.1080/02664763.2013.864263>.
Package details |
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Author | Erin LeDell [cre], Stephanie Sapp [aut], Mark van der Laan [aut] |
Maintainer | Erin LeDell <oss@ledell.org> |
License | Apache License (== 2.0) |
Version | 0.1.0 |
URL | https://github.com/ledell/subsemble |
Package repository | View on CRAN |
Installation |
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