Package: themis 1.0.3.9000

Emil Hvitfeldt

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 <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>.

Authors:Emil Hvitfeldt [aut, cre], Posit Software, PBC [cph, fnd]

themis_1.0.3.9000.tar.gz
themis_1.0.3.9000.zip(r-4.7-any)themis_1.0.3.9000.zip(r-4.6-any)themis_1.0.3.9000.zip(r-4.5-any)
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
themis/json (API)

# Install 'themis' in R:
install.packages('themis', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/themis/issues

Pkgdown/docs site:https://themis.tidymodels.org

Datasets:

On CRAN:

Conda:

10.70 score 142 stars 2 packages 2.0k scripts 10k downloads 35 exports 65 dependencies

Last updated from:074391ef26. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK241
source / vignettesOK220
linux-release-x86_64OK255
macos-release-arm64OK179
macos-oldrel-arm64OK129
windows-devel-x86_64OK210
windows-release-x86_64OK212
windows-oldrel-x86_64OK201
wasm-releaseOK130

Exports:adasynbsmotecnnenninstance_hardnessnclnearmissossrequired_pkgsrosesmognsmotesmotensmotencstep_adasynstep_bsmotestep_cnnstep_downsamplestep_ennstep_instance_hardnessstep_nclstep_nearmissstep_ossstep_rosestep_smognstep_smotestep_smotenstep_smotencstep_svmsmotestep_tomekstep_upsamplesvmsmotetidytomektunable

Dependencies:classcliclockcodetoolscpp11data.tablediagramdigestdplyrfarverfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobandKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixnnetnumDerivparallellypillarpkgconfigprodlimprogressrpurrrR6RANNRColorBrewerRcpprecipesrlangROSErpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Adaptive Synthetic Algorithmadasyn
borderline-SMOTE Algorithmbsmote
Synthetic Dataset With a Circlecircle_example
Condensed Nearest Neighborscnn
Edited Nearest Neighborsenn
Remove hard to classify pointsinstance_hardness
Neighborhood Cleaning Rulencl
Remove Points Near Other Classesnearmiss
One-Sided Selectionoss
ROSE Algorithmrose
SMOGN Algorithmsmogn
SMOTE Algorithmsmote
SMOTEN Algorithmsmoten
SMOTENC Algorithmsmotenc
Apply Adaptive Synthetic Algorithmstep_adasyn tidy.step_adasyn
Apply borderline-SMOTE Algorithmstep_bsmote tidy.step_bsmote
Condensed Nearest Neighborsstep_cnn tidy.step_cnn
Down-Sample a Data Set Based on a Factor Variablestep_downsample tidy.step_downsample
Edited Nearest Neighborsstep_enn tidy.step_enn
Remove hard to classify pointsstep_instance_hardness tidy.step_instance_hardness
Neighborhood Cleaning Rulestep_ncl tidy.step_ncl
Remove Points Near Other Classesstep_nearmiss tidy.step_nearmiss
One-Sided Selectionstep_oss tidy.step_oss
Apply ROSE Algorithmstep_rose tidy.step_rose
Apply SMOGN Algorithmstep_smogn tidy.step_smogn
Apply SMOTE Algorithmstep_smote tidy.step_smote
Apply SMOTEN algorithmstep_smoten tidy.step_smoten
Apply SMOTENC algorithmstep_smotenc tidy.step_smotenc
Apply SVM-SMOTE Algorithmstep_svmsmote tidy.step_svmsmote
Remove Tomek’s Linksstep_tomek tidy.step_tomek
Up-Sample a Data Set Based on a Factor Variablestep_upsample tidy.step_upsample
SVM-SMOTE Algorithmsvmsmote
Remove Tomek's linkstomek