Package: autoEnsemble 0.3

E. F. Haghish

autoEnsemble: Automated Stacked Ensemble Classifier for Severe Class Imbalance

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.

Authors:E. F. Haghish [aut, cre, cph]

autoEnsemble_0.3.tar.gz
autoEnsemble_0.3.zip(r-4.7)autoEnsemble_0.3.zip(r-4.6)autoEnsemble_0.3.zip(r-4.5)
autoEnsemble_0.3.tgz(r-4.6-any)autoEnsemble_0.3.tgz(r-4.5-any)
autoEnsemble_0.3.tar.gz(r-4.7-any)autoEnsemble_0.3.tar.gz(r-4.6-any)
autoEnsemble_0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
autoEnsemble/json (API)

# Install 'autoEnsemble' in R:
install.packages('autoEnsemble', repos = c('https://haghish.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/haghish/autoensemble/issues

On CRAN:

Conda:

aialgorithmautomated-machine-learningautomlautoml-algorithmsensembleensemble-learningh2oh2oaimachine-learningmachinelearningmetalearningstack-ensemblestacked-ensemblesstacking

4.34 score 7 stars 1 packages 21 scripts 278 downloads 5 exports 7 dependencies

Last updated from:d83a0dcf15. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK140
source / vignettesOK185
linux-release-x86_64OK121
macos-release-arm64OK127
macos-oldrel-arm64OK104
windows-develOK81
windows-releaseOK62
windows-oldrelOK68
wasm-releaseOK110

Exports:autoEnsembleensembleevaluateh2o.get_idsmodelSelection

Dependencies:bitopsbootcurlh2oh2otoolsjsonliteRCurl