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>.
| 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 |
Documentation:
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