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A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.
Version: | 0.3 |
Depends: | R (≥ 3.5.0) |
Imports: | h2o (≥ 3.34.0.0), h2otools (≥ 0.3), curl (≥ 4.3.0) |
Published: | 2025-03-20 |
DOI: | 10.32614/CRAN.package.autoEnsemble |
Author: | E. F. Haghish [aut, cre, cph] |
Maintainer: | E. F. Haghish <haghish at hotmail.com> |
BugReports: | https://github.com/haghish/autoEnsemble/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/haghish/autoEnsemble, https://www.sv.uio.no/psi/english/people/academic/haghish/ |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | autoEnsemble results |
Reference manual: | autoEnsemble.pdf |
Package source: | autoEnsemble_0.3.tar.gz |
Windows binaries: | r-devel: autoEnsemble_0.3.zip, r-release: autoEnsemble_0.3.zip, r-oldrel: autoEnsemble_0.3.zip |
macOS binaries: | r-devel (arm64): autoEnsemble_0.3.tgz, r-release (arm64): autoEnsemble_0.3.tgz, r-oldrel (arm64): autoEnsemble_0.3.tgz, r-devel (x86_64): autoEnsemble_0.3.tgz, r-release (x86_64): autoEnsemble_0.3.tgz, r-oldrel (x86_64): autoEnsemble_0.3.tgz |
Old sources: | autoEnsemble archive |
Reverse imports: | HMDA |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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