Package: themis 1.0.2.9000
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 <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:
themis_1.0.2.9000.tar.gz
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themis_1.0.2.9000.tgz(r-4.4-any)themis_1.0.2.9000.tgz(r-4.3-any)
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themis.pdf |themis.html✨
themis/json (API)
NEWS
| # 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:https://themis.tidymodels.org
- circle_example - Synthetic Dataset With a Circle
Last updated 1 months agofrom:95e578bbe9. Checks:OK: 5 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 09 2024 |
| R-4.5-win | NOTE | Dec 09 2024 |
| R-4.5-linux | NOTE | Dec 09 2024 |
| R-4.4-win | OK | Dec 09 2024 |
| R-4.4-mac | OK | Dec 09 2024 |
| R-4.3-win | OK | Dec 09 2024 |
| R-4.3-mac | OK | Dec 09 2024 |
Exports:adasynbsmotenearmissrequired_pkgssmotesmotencstep_adasynstep_bsmotestep_downsamplestep_nearmissstep_rosestep_smotestep_smotencstep_tomekstep_upsampletidytomektunable
Dependencies:classcliclockcodetoolscpp11data.tablediagramdigestdplyrfansifuturefuture.applygenericsglobalsgluegowerhardhatipredKernSmoothlatticelavalifecyclelistenvlubridatemagrittrMASSMatrixnnetnumDerivparallellypillarpkgconfigprodlimprogressrpurrrR6RANNRcpprecipesrlangROSErpartshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrswithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Adaptive Synthetic Algorithm | adasyn |
| borderline-SMOTE Algorithm | bsmote |
| Synthetic Dataset With a Circle | circle_example |
| Remove Points Near Other Classes | nearmiss |
| SMOTE Algorithm | smote |
| SMOTENC Algorithm | smotenc |
| Apply Adaptive Synthetic Algorithm | step_adasyn tidy.step_adasyn |
| Apply borderline-SMOTE Algorithm | step_bsmote tidy.step_bsmote |
| Down-Sample a Data Set Based on a Factor Variable | step_downsample tidy.step_downsample |
| Remove Points Near Other Classes | step_nearmiss tidy.step_nearmiss |
| Apply ROSE Algorithm | step_rose tidy.step_rose |
| Apply SMOTE Algorithm | step_smote tidy.step_smote |
| Apply SMOTENC algorithm | step_smotenc tidy.step_smotenc |
| Remove Tomek’s Links | step_tomek tidy.step_tomek |
| Up-Sample a Data Set Based on a Factor Variable | step_upsample tidy.step_upsample |
| Remove Tomek's links | tomek |
