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 <doi:10.48550/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: 1.0.2
Depends: R (≥ 3.6), recipes (≥ 1.0.4)
Imports: gower, lifecycle (≥ 1.0.3), dplyr, generics (≥ 0.1.0), purrr, RANN, rlang, ROSE, tibble, withr, glue, hardhat, vctrs
Suggests: covr, dials (≥ 1.2.0), ggplot2, modeldata, testthat (≥ 3.0.0)
Published: 2023-08-14
DOI: 10.32614/CRAN.package.themis
Author: Emil Hvitfeldt ORCID iD [aut, cre], Posit Software, PBC [cph, fnd]
Maintainer: Emil Hvitfeldt <emil.hvitfeldt at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: themis results


Reference manual: themis.pdf


Package source: themis_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): themis_1.0.2.tgz, r-oldrel (arm64): themis_1.0.2.tgz, r-release (x86_64): themis_1.0.2.tgz, r-oldrel (x86_64): themis_1.0.2.tgz
Old sources: themis archive

Reverse dependencies:

Reverse imports: pheble
Reverse suggests: caret


Please use the canonical form to link to this page.