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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 "EZtune" in publications use:'
type: software
license: GPL-3.0-only
title: 'EZtune: Tunes AdaBoost, Elastic Net, Support Vector Machines, and Gradient
  Boosting Machines'
version: 3.1.2
identifiers:
- type: doi
  value: 10.32614/CRAN.package.EZtune
abstract: Contains two functions that are intended to make tuning supervised learning
  methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer
  to find the best set of tuning parameters. The user can choose the optimizer, the
  learning method, and if optimization will be based on accuracy obtained through
  validation error, cross validation, or resubstitution. The function eztune_cv will
  compute a cross validated error rate. The purpose of eztune_cv is to provide a cross
  validated accuracy or MSE when resubstitution or validation data are used for optimization
  because error measures from both approaches can be misleading.
authors:
- family-names: Lundell
  given-names: Jill
  email: jflundell@gmail.com
preferred-citation:
  type: article
  title: 'There has to be an easier way: a simple alternative for parameter tuning
    of supervised learning methods'
  authors:
  - family-names: Lundell
    given-names: Jill F
  journal: 'JSM Proceedings, Statistical Computing Section. Alexandria, VA: American
    Statistical Association'
  year: '2017'
  start: '3028'
  end: '3036'
repository: https://jillbo1000.r-universe.dev
commit: 6fe00c1d0ddb690786083d31f610a55bef88a2ce
contact:
- family-names: Lundell
  given-names: Jill
  email: jflundell@gmail.com