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ensembleML  

Unified Interface for Ensemble Machine Learning Methods
View on CRAN: Click here


Download and install ensembleML package within the R console
Install from CRAN:
install.packages("ensembleML")

Install from Github:
library("remotes")
install_github("cran/ensembleML")

Install by package version:
library("remotes")
install_version("ensembleML", "0.2.5")



Attach the package and use:
library("ensembleML")
Maintained by
Sadikul Islam
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-05
Latest Update: 2026-06-05
Description:
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>.
How to cite:
Sadikul Islam (2026). ensembleML: Unified Interface for Ensemble Machine Learning Methods. R package version 0.2.5, https://cran.r-project.org/web/packages/ensembleML. Accessed 04 Oct. 2026.
Previous versions and publish date:
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Complete documentation for ensembleML
Functions, R codes and Examples using the ensembleML R package
Full ensembleML package functions and examples
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