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:
autoEnsemble_0.3.tar.gz
autoEnsemble_0.3.zip(r-4.5)autoEnsemble_0.3.zip(r-4.4)autoEnsemble_0.3.zip(r-4.3)
autoEnsemble_0.3.tgz(r-4.5-any)autoEnsemble_0.3.tgz(r-4.4-any)autoEnsemble_0.3.tgz(r-4.3-any)
autoEnsemble_0.3.tar.gz(r-4.5-noble)autoEnsemble_0.3.tar.gz(r-4.4-noble)
autoEnsemble_0.3.tgz(r-4.4-emscripten)autoEnsemble_0.3.tgz(r-4.3-emscripten)
autoEnsemble.pdf |autoEnsemble.html✨
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
aialgorithmautomated-machine-learningautomlautoml-algorithmsensembleensemble-learningh2oh2oaimachine-learningmachinelearningmetalearningstack-ensemblestacked-ensemblesstacking
Last updated 1 days agofrom:d83a0dcf15. Checks:9 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 24 2025 |
R-4.5-win | OK | Mar 24 2025 |
R-4.5-mac | OK | Mar 24 2025 |
R-4.5-linux | OK | Mar 24 2025 |
R-4.4-win | OK | Mar 24 2025 |
R-4.4-mac | OK | Mar 24 2025 |
R-4.4-linux | OK | Mar 24 2025 |
R-4.3-win | OK | Mar 24 2025 |
R-4.3-mac | OK | Mar 24 2025 |
Exports:autoEnsembleensembleevaluateh2o.get_idsmodelSelection
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Automatically Trains H2O Models and Builds a Stacked Ensemble Model | autoEnsemble |
Builds Stacked Ensemble Model from H2O Models | ensemble |
Evaluate H2O Model(s) Performance | evaluate |
h2o.get_ids | h2o.get_ids |
Selects Diverse Top-Performing Models for Stacking an Ensemble Model | modelSelection |
Stopping Criteria for Ending the Search | stopping_criteria |