autoEnsemble: Automated Stacked Ensemble Classifier for Severe Class Imbalance

An AutoML algorithm is developed to construct homogeneous or heterogeneous stacked ensemble models using specified base-learners. Various criteria are employed to identify optimal models, enhancing diversity among them and resulting in more robust stacked ensembles. The algorithm optimizes the model by incorporating an increasing number of top-performing models to create a diverse combination. Presently, only models from 'h2o.ai' are supported.

Version: 0.2
Depends: R (≥ 3.5.0)
Imports: h2o (≥ 3.34.0.0), h2otools (≥ 0.3), curl (≥ 4.3.0)
Published: 2023-05-09
Author: E. F. Haghish [aut, cre, cph]
Maintainer: E. F. Haghish <haghish at uio.no>
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

Documentation:

Reference manual: autoEnsemble.pdf

Downloads:

Package source: autoEnsemble_0.2.tar.gz
Windows binaries: r-prerel: autoEnsemble_0.2.zip, r-release: autoEnsemble_0.2.zip, r-oldrel: autoEnsemble_0.2.zip
macOS binaries: r-prerel (arm64): autoEnsemble_0.2.tgz, r-release (arm64): autoEnsemble_0.2.tgz, r-oldrel (arm64): autoEnsemble_0.2.tgz, r-prerel (x86_64): autoEnsemble_0.2.tgz, r-release (x86_64): autoEnsemble_0.2.tgz

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