Package: vtreat 1.6.5
vtreat: A Statistically Sound 'data.frame' Processor/Conditioner
A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. 'vtreat' prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems 'vtreat' defends against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: "'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <doi:10.5281/zenodo.1173313>.
Authors:
vtreat_1.6.5.tar.gz
vtreat_1.6.5.zip(r-4.5)vtreat_1.6.5.zip(r-4.4)vtreat_1.6.5.zip(r-4.3)
vtreat_1.6.5.tgz(r-4.4-any)vtreat_1.6.5.tgz(r-4.3-any)
vtreat_1.6.5.tar.gz(r-4.5-noble)vtreat_1.6.5.tar.gz(r-4.4-noble)
vtreat_1.6.5.tgz(r-4.4-emscripten)vtreat_1.6.5.tgz(r-4.3-emscripten)
vtreat.pdf |vtreat.html✨
vtreat/json (API)
NEWS
# Install 'vtreat' in R: |
install.packages('vtreat', repos = c('https://winvector.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/winvector/vtreat/issues
categorical-variablesmachine-learning-algorithmsnested-modelsprepare-data
Last updated 5 months agofrom:9e28ee2eae. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 10 2024 |
R-4.5-win | OK | Nov 10 2024 |
R-4.5-linux | OK | Nov 10 2024 |
R-4.4-win | OK | Nov 10 2024 |
R-4.4-mac | OK | Nov 10 2024 |
R-4.3-win | OK | Nov 10 2024 |
R-4.3-mac | OK | Nov 10 2024 |
Exports:.wmeanapply_transformas_rquery_planBinomialOutcomeTreatmentbuildEvalSetscenter_scaleclassification_parametersdesign_missingness_treatmentdesignTreatmentsCdesignTreatmentsNdesignTreatmentsZfitfit_preparefit_transformflatten_fn_listget_feature_namesget_score_frameget_transformgetSplitPlanAppLabelskWayCrossValidationkWayStratifiedYkWayStratifiedYReplacemakekWayCrossValidationGroupedByColumnmaterialize_treatedmkCrossFrameCExperimentmkCrossFrameMExperimentmkCrossFrameNExperimentmultinomial_parametersMultinomialOutcomeTreatmentnovel_value_summaryNumericOutcomeTreatmentoneWayHoldoutpatch_columns_into_framepre_comp_xvalprepareproblemAppPlanregression_parametersrqdatatable_preparerquery_preparesolve_piecewisesolve_piecewisecspline_variablespline_variablecsquare_windowsquare_windowctrack_valuesunsupervised_parametersUnsupervisedTreatmentvalue_variables_Cvalue_variables_Nvariable_valuesvnamesvorig
Multi Class vtreat
Rendered fromMultiClassVtreat.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2018-10-01
Started: 2018-07-15
Saving Treatment Plans
Rendered fromSavingTreamentPlans.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2017-01-05
Variable Types
Rendered fromvtreatVariableTypes.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2016-03-18
vtreat cross frames
Rendered fromvtreatCrossFrames.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2016-04-08
vtreat data splitting
Rendered fromvtreatSplitting.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2016-06-13
vtreat Formal Article
Rendered fromvtreat_article.pdf.asis
usingR.rsp::asis
on Nov 10 2024.Last update: 2018-11-05
Started: 2018-11-05
vtreat grouping example
Rendered fromvtreatGrouping.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2016-06-15
vtreat overfit
Rendered fromvtreatOverfit.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2015-09-08
vtreat package
Rendered fromvtreat.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2015-01-20
vtreat Rare Levels
Rendered fromvtreatRareLevels.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2019-03-31
Started: 2016-09-29
vtreat scale mode
Rendered fromvtreatScaleMode.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2016-04-18
vtreat significance
Rendered fromvtreatSignificance.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2016-05-07
vtreat Variable Importance
Rendered fromVariableImportance.Rmd
usingknitr::rmarkdown
on Nov 10 2024.Last update: 2020-08-12
Started: 2018-12-18