ensembleML: Unified Interface for Ensemble Machine Learning Methods
Provides a clean, unified interface for training, predicting,
and evaluating ensemble machine learning models including Random Forest,
Gradient Boosting ('XGBoost'), 'AdaBoost', and 'Bagging'. All algorithms share
a consistent API: em_fit(), em_predict(), em_evaluate(), and em_tune().
Includes built-in cross-validation, feature importance, calibration
diagnostics, partial dependence plots, and model comparison utilities.
Methods: Breiman (2001) <doi:10.1023/A:1010933404324>;
Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>;
Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>;
Breiman (1996) <doi:10.1007/BF00058655>.
| Version: |
0.2.5 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
randomForest (≥ 4.7-1), xgboost (≥ 1.7.0), adabag (≥ 4.2), ggplot2 (≥ 3.4.0), rlang (≥ 1.1.0), stats, utils |
| Suggests: |
pROC (≥ 1.18.0), gridExtra (≥ 2.3), testthat (≥ 3.0.0), knitr, rmarkdown, mlbench |
| Published: |
2026-06-05 |
| DOI: |
10.32614/CRAN.package.ensembleML (may not be active yet) |
| Author: |
Sadikul Islam 
[aut, cre] |
| Maintainer: |
Sadikul Islam <sadikul.islamiasri at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
ensembleML citation info |
| CRAN checks: |
ensembleML results |
Documentation:
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