A data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. 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, new categorical levels (levels seen during application, but not during training). 'vtreat::prepare' should be used as you would use 'model.matrix'.
Version: | 0.5.28 |
Suggests: | testthat, knitr, parallel, rmarkdown, dplyr, ggplot2 |
Published: | 2016-10-25 |
Author: | John Mount, Nina Zumel |
Maintainer: | John Mount <jmount at win-vector.com> |
BugReports: | https://github.com/WinVector/vtreat/issues |
License: | GPL-3 |
URL: | https://github.com/WinVector/vtreat |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | vtreat results |
Reference manual: | vtreat.pdf |
Vignettes: |
vtreat package vtreat cross frames vtreat grouping example vtreat overfit vtreat Rare Levels vtreat scale mode vtreat significance vtreat data splitting Variable Types |
Package source: | vtreat_0.5.28.tar.gz |
Windows binaries: | r-devel: vtreat_0.5.27.zip, r-release: vtreat_0.5.27.zip, r-oldrel: vtreat_0.5.27.zip |
OS X Mavericks binaries: | r-release: vtreat_0.5.27.tgz, r-oldrel: vtreat_0.5.27.tgz |
Old sources: | vtreat archive |
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