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'.
|Suggests:||testthat, knitr, parallel, rmarkdown, dplyr, ggplot2, RSQLite|
|Author:||John Mount, Nina Zumel|
|Maintainer:||John Mount <jmount at win-vector.com>|
|CRAN checks:||vtreat results|
Saving Treatment Plans
vtreat cross frames
vtreat grouping example
vtreat Rare Levels
vtreat scale mode
vtreat data splitting
|Windows binaries:||r-devel: vtreat_0.5.31.zip, r-release: vtreat_0.5.31.zip, r-oldrel: vtreat_0.5.31.zip|
|OS X El Capitan binaries:||r-release: vtreat_0.5.31.tgz|
|OS X Mavericks binaries:||r-oldrel: vtreat_0.5.31.tgz|
|Old sources:||vtreat archive|
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