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metaEnsembleR  

Automated Intuitive Package for Meta-Ensemble Learning
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("metaEnsembleR", "0.1.0")



Attach the package and use:
library("metaEnsembleR")
Maintained by
Ajay Arunachalam
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-11-19
Latest Update: 2020-11-19
Description:
Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.
How to cite:
Ajay Arunachalam (2020). metaEnsembleR: Automated Intuitive Package for Meta-Ensemble Learning. R package version 0.1.0, https://cran.r-project.org/web/packages/metaEnsembleR. Accessed 02 Feb. 2025.
Previous versions and publish date:
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Complete documentation for metaEnsembleR
Functions, R codes and Examples using the metaEnsembleR R package
Some associated functions: ensembler.classifier . ensembler.regression . 
Some associated R codes: metaEnsemble.R .  Full metaEnsembleR package functions and examples
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