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 <>. Or by decreasing the number of majority cases using NearMiss 2003 <> or Tomek link removal 1976 <>.

Version: 0.1.0
Depends: R (≥ 2.10), recipes (≥ 0.1.4)
Imports: tibble, purrr, withr, generics, dplyr, rlang, tidyselect (≥ 0.2.5), ROSE, unbalanced, RANN, dials
Suggests: testthat (≥ 2.1.0), covr, ggplot2, modeldata
Published: 2020-01-13
Author: Emil Hvitfeldt ORCID iD [aut, cre]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: themis results


Reference manual: themis.pdf
Package source: themis_0.1.0.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: themis_0.1.0.tgz, r-oldrel: themis_0.1.0.tgz


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