woeBinning: Supervised Weight of Evidence Binning of Numeric Variables and Factors

Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.

Version: 0.1.4
Published: 2017-06-12
Author: Thilo Eichenberg
Maintainer: Thilo Eichenberg <te.r at gmx.net>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: woeBinning results


Reference manual: woeBinning.pdf
Package source: woeBinning_0.1.4.tar.gz
Windows binaries: r-devel: woeBinning_0.1.4.zip, r-release: woeBinning_0.1.4.zip, r-oldrel: woeBinning_0.1.4.zip
OS X El Capitan binaries: r-release: woeBinning_0.1.4.tgz
OS X Mavericks binaries: r-oldrel: woeBinning_0.1.4.tgz
Old sources: woeBinning archive

Reverse dependencies:

Reverse imports: HybridFS


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