themis: Extra Recipes Steps for Dealing with Unbalanced Data

A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 <arXiv:1106.1813>, BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008 <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the number of majority cases using NearMiss 2003 <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.

Version: 0.1.3
Depends: R (≥ 2.10), recipes (≥ 0.1.15)
Imports: dplyr, generics (≥ 0.1.0), purrr, RANN, rlang, ROSE, tibble, unbalanced, withr
Suggests: covr, ggplot2, modeldata, testthat (≥ 2.1.0)
Published: 2020-11-12
Author: Emil Hvitfeldt ORCID iD [aut, cre]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt at gmail.com>
BugReports: https://github.com/tidymodels/themis/issues
License: MIT + file LICENSE
URL: https://github.com/tidymodels/themis, https://themis.tidymodels.org
NeedsCompilation: no
Materials: README NEWS
CRAN checks: themis results

Downloads:

Reference manual: themis.pdf
Package source: themis_0.1.3.tar.gz
Windows binaries: r-devel: themis_0.1.3.zip, r-release: themis_0.1.3.zip, r-oldrel: themis_0.1.3.zip
macOS binaries: r-release: themis_0.1.3.tgz, r-oldrel: themis_0.1.3.tgz
Old sources: themis archive

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