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cff-version: 1.2.0
message: 'To cite package "metaEnsembleR" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'metaEnsembleR: Automated Intuitive Package for Meta-Ensemble Learning'
version: 0.1.0
doi: 10.32614/CRAN.package.metaEnsembleR
abstract: 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.
authors:
- family-names: Arunachalam
  given-names: Ajay
  email: ajay.arunachalam08@gmail.com
repository: https://cranhaven.r-universe.dev
commit: 0ac3f147ae732f35fdb07e81806be456c3e5682e
date-released: '2020-10-27'
contact:
- family-names: Arunachalam
  given-names: Ajay
  email: ajay.arunachalam08@gmail.com