ledell/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>.
Getting started
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Package details |
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| Maintainer | |
| License | Apache License (== 2.0) |
| Version | 0.1.0 |
| URL | https://github.com/ledell/subsemble |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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