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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=subsemble to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4