subsemble-package: An Ensemble Method for Combining Subset-Specific Algorithm...

In ledell/subsemble: An Ensemble Method for Combining Subset-Specific Algorithm Fits

Description

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.

Details

Package:	subsemble
Type:	Package
Version:	0.1.0
Date:	2012-01-22
License:	Apache License (== 2.0)

Note

This work was supported in part by the Doris Duke Charitable Foundation Grant No. 2011042.

Author(s)

Authors: Erin LeDell, Stephanie Sapp, Mark van der Laan

Maintainer: Erin LeDell <[e-mail]>

References

LeDell, E. (2015) Scalable Ensemble Learning and Computationally Efficient Variance Estimation (Doctoral Dissertation). University of California, Berkeley, USA.
https://github.com/ledell/phd-thesis/blob/main/ledell-phd-thesis.pdf

Stephanie Sapp, Mark J. van der Laan & John Canny. (2014) Subsemble: An ensemble method for combining subset-specific algorithm fits. Journal of Applied Statistics, 41(6):1247-1259.
https://www.tandfonline.com/doi/abs/10.1080/02664763.2013.864263
https://biostats.bepress.com/ucbbiostat/paper313/

See Also

SuperLearner

ledell/subsemble documentation built on Jan. 24, 2022, 5:38 p.m.