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