metaEnsembleR: Automated Intuitive Package for Meta-Ensemble Learning

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 <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> 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.

Version: 0.1.0
Depends: gridExtra
Imports: caret, ggplot2, graphics, e1071, gbm, randomForest
Suggests: knitr, R.rsp
Published: 2020-11-19
Author: Ajay Arunachalam
Maintainer: Ajay Arunachalam <ajay.arunachalam08 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: metaEnsembleR results

Downloads:

Reference manual: metaEnsembleR.pdf
Vignettes: Intuitive Package for Meta-Ensemble Learning (Classification, Regression) that is Fully-Automated
Package source: metaEnsembleR_0.1.0.tar.gz
Windows binaries: r-devel: metaEnsembleR_0.1.0.zip, r-release: metaEnsembleR_0.1.0.zip, r-oldrel: metaEnsembleR_0.1.0.zip
macOS binaries: r-release: metaEnsembleR_0.1.0.tgz, r-oldrel: metaEnsembleR_0.1.0.tgz

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