subsemble: An Ensemble Method for Combining Subset-Specific Algorithm Fits

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

AuthorErin LeDell [cre], Stephanie Sapp [aut], Mark van der Laan [aut]
MaintainerErin LeDell <oss@ledell.org>
LicenseApache License (== 2.0)
Version0.1.0
URL https://github.com/ledell/subsemble
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("subsemble")

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subsemble documentation built on Jan. 25, 2022, 1:06 a.m.