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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>.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.14.0), SuperLearner |
| Suggests: | arm, caret, class, cvAUC, e1071, earth, gam, gbm, glmnet, Hmisc, ipred, lattice, LogicReg, MASS, mda, mlbench, nnet, parallel, party, polspline, quadprog, randomForest, rpart, SIS, spls, stepPlr |
| Published: | 2022-01-24 |
| DOI: | 10.32614/CRAN.package.subsemble |
| Author: | Erin LeDell [cre], Stephanie Sapp [aut], Mark van der Laan [aut] |
| Maintainer: | Erin LeDell <oss at ledell.org> |
| BugReports: | https://github.com/ledell/subsemble/issues |
| License: | Apache License (== 2.0) |
| URL: | https://github.com/ledell/subsemble |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | subsemble results |
| Reference manual: | subsemble.pdf |
| Package source: | subsemble_0.1.0.tar.gz |
| Windows binaries: | r-devel: subsemble_0.1.0.zip, r-release: subsemble_0.1.0.zip, r-oldrel: subsemble_0.1.0.zip |
| macOS binaries: | r-release (arm64): subsemble_0.1.0.tgz, r-oldrel (arm64): subsemble_0.1.0.tgz, r-release (x86_64): subsemble_0.1.0.tgz, r-oldrel (x86_64): subsemble_0.1.0.tgz |
| Old sources: | subsemble archive |
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