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 |
| Reference manual: | autoEnsemble.pdf |
| Package source: | autoEnsemble_0.2.tar.gz |
| Windows binaries: | r-devel: autoEnsemble_0.2.zip, r-release: autoEnsemble_0.2.zip, r-oldrel: autoEnsemble_0.2.zip |
| macOS binaries: | r-release (arm64): autoEnsemble_0.2.tgz, r-oldrel (arm64): autoEnsemble_0.2.tgz, r-release (x86_64): autoEnsemble_0.2.tgz, r-oldrel (x86_64): autoEnsemble_0.2.tgz |
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