Package: woeBinning 0.1.6
Thilo Eichenberg
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.
Authors:
woeBinning_0.1.6.tar.gz
woeBinning_0.1.6.tar.gz(r-4.5-noble)woeBinning_0.1.6.tar.gz(r-4.4-noble)
woeBinning_0.1.6.tgz(r-4.4-emscripten)woeBinning_0.1.6.tgz(r-4.3-emscripten)
woeBinning.pdf |woeBinning.html✨
woeBinning/json (API)
| # Install 'woeBinning' in R: |
| install.packages('woeBinning', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- germancredit - German Credit Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:a75966e200. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 04 2024 |
| R-4.5-linux | OK | Nov 04 2024 |
Exports:woe.binningwoe.binning.deploywoe.binning.plotwoe.binning.tablewoe.tree.binning
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| German Credit Data | germancredit |
| Binning via Fine and Coarse Classing | woe.binning |
| Deployment of Binning | woe.binning.deploy |
| Visualization of Binning | woe.binning.plot |
| Tabulation of Binning | woe.binning.table |
| Binning via Tree-Like Segmentation | woe.tree.binning |
| Package for Supervised Weight of Evidence Binning | woeBinning |