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). 'vtreat::prepare' should be used as you would use 'model.matrix'.

Maintainer: John Mount <jmount at win-vector.com>

Author(s): John Mount*, Nina Zumel*, Win-Vector LLC*

Install package and any missing dependencies by running this line in your R console:

install.packages("vtreat")

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Suggests testthat, knitr, parallel, rmarkdown, dplyr, ggplot2, RSQLite, datasets, stats
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Package vtreat
Materials
URL https://github.com/WinVector/vtreat/ https://winvector.github.io/vtreat/
Task Views
Version 1.0.1
Published 2017-10-17
License GPL-3
BugReports https://github.com/WinVector/vtreat/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks vtreat check results
Package source vtreat_1.0.1.tar.gz