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>.


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install.packages("themis")

0.1.0 by Emil Hvitfeldt, a month ago


https://github.com/tidymodels/themis


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 tibble, purrr, withr, generics, dplyr, rlang, tidyselect, ROSE, unbalanced, RANN, dials

Depends on recipes

Suggests testthat, covr, ggplot2, modeldata


See at CRAN