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 , BorderlineSMOTE 2005 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>.


Reference manual

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0.1.2 by Emil Hvitfeldt, a month ago

https://github.com/tidymodels/themis, https://themis.tidymodels.org

Report a bug at https://github.com/tidymodels/themis/issues

Browse source code at https://github.com/cran/themis

Authors: Emil Hvitfeldt [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports dplyr, generics, purrr, RANN, rlang, ROSE, tibble, unbalanced, withr

Depends on recipes

Suggests covr, ggplot2, modeldata, testthat

See at CRAN