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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "EnsembleBase" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'EnsembleBase: Extensible Package for Parallel, Batch Training of Base Learners
  for Ensemble Modeling'
version: 1.0.2
doi: 10.32614/CRAN.package.EnsembleBase
abstract: Extensible S4 classes and methods for batch training of regression and classification
  algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support
  Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian
  Additive Regression Trees. These algorithms constitute a set of 'base learners',
  which can subsequently be combined together to form ensemble predictions. This package
  provides cross-validation wrappers to allow for downstream application of ensemble
  integration techniques, including best-error selection. All base learner estimation
  objects are retained, allowing for repeated prediction calls without the need for
  re-training. For large problems, an option is provided to save estimation objects
  to disk, along with prediction methods that utilize these objects. This allows users
  to train and predict with large ensembles of base learners without being constrained
  by system RAM.
authors:
- family-names: Mahani
  given-names: Alireza S.
  email: alireza.s.mahani@gmail.com
- family-names: Mahani
  given-names: Alireza S.
- family-names: Sharabiani
  given-names: Mansour T.A.
repository: https://asmahani.r-universe.dev
commit: c9a98b3fe0c06512d598c4e341135b90d88cd517
date-released: '2016-09-13'
contact:
- family-names: Mahani
  given-names: Alireza S.
  email: alireza.s.mahani@gmail.com