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.1173314.

Version: 1.0.3
Imports: stats
Suggests: testthat, knitr, parallel, rmarkdown, dplyr, ggplot2, RSQLite, datasets
Published: 2018-03-10
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount at win-vector.com>
BugReports: https://github.com/WinVector/vtreat/issues
License: GPL-3
URL: https://github.com/WinVector/vtreat/, https://winvector.github.io/vtreat/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: vtreat results

Downloads:

Reference manual: vtreat.pdf
Vignettes: Saving Treatment Plans
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_1.0.3.tar.gz
Windows binaries: r-prerel: vtreat_1.0.3.zip, r-release: vtreat_1.0.3.zip, r-oldrel: vtreat_1.0.3.zip
OS X binaries: r-prerel: vtreat_1.0.3.tgz, r-release: vtreat_1.0.3.tgz
Old sources: vtreat archive

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