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>.

Package overview README.md Multi Class vtreat Saving Treatment Plans Variable Types vtreat cross frames vtreat data splitting vtreat grouping example vtreat overfit vtreat package vtreat Rare Levels vtreat scale mode vtreat significance vtreat Variable Importance

Vignettes Man pages API and functions Files

Package details
Author	John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer	John Mount <jmount@win-vector.com>
License	GPL-2 \| GPL-3
Version	1.6.4
URL	https://github.com/WinVector/vtreat/ https://winvector.github.io/vtreat/
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("vtreat")`