Package: subsemble 0.1.0

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>.

Authors:Erin LeDell [cre], Stephanie Sapp [aut], Mark van der Laan [aut]

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Bug tracker:https://github.com/ledell/subsemble/issues

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big-datacross-validationensembleensemble-learningmachine-learningmachine-learning-algorithms

2 exports 43 stars 2.87 score 10 dependencies 11 scripts 318 downloads

Last updated 3 years agofrom:93a0acd027. Checks:OK: 7. Indexed: yes.

Exports:predict.subsemblesubsemble

Dependencies:bitopscaToolscvAUCdata.tablegplotsgtoolsKernSmoothnnlsROCRSuperLearner