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: | 0.1.0 |
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 |
Maintainer: | Emil Hvitfeldt <emilhhvitfeldt at gmail.com> |
BugReports: | https://github.com/tidymodels/themis/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/tidymodels/themis |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | themis results |
Reference manual: | themis.pdf |
Package source: | themis_0.1.0.tar.gz |
Windows binaries: | r-devel: themis_0.1.0.zip, r-devel-gcc8: themis_0.1.0.zip, r-release: themis_0.1.0.zip, r-oldrel: themis_0.1.0.zip |
OS X binaries: | r-release: themis_0.1.0.tgz, r-oldrel: themis_0.1.0.tgz |
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