OptimalBinningWoE (version 1.0.8)

ob_cutpoints_cat: Binning Categorical Variables using Custom Cutpoints

Description

This function applies user-defined binning to a categorical variable by grouping specified categories into bins and calculating Weight of Evidence (WoE) and Information Value (IV) for each bin.

Usage

ob_cutpoints_cat(feature, target, cutpoints)

Value

A list containing:

woefeature

Numeric vector of WoE values corresponding to each observation in the input feature

woebin

Data frame with one row per bin containing:

• bin: The bin definition (original categories joined by "+")

• count: Total number of observations in the bin

• count_pos: Number of positive outcomes (target=1) in the bin

• count_neg: Number of negative outcomes (target=0) in the bin

• woe: Weight of Evidence for the bin

• iv: Information Value contribution of the bin

Arguments

feature

A character vector or factor representing the categorical predictor variable.

target

An integer vector containing binary outcome values (0 or 1). Must be the same length as feature.

cutpoints

A character vector where each element defines a bin by concatenating the original category names with "+" as separator.

Details

The function takes a character vector defining how categories should be grouped. Each element in the cutpoints vector defines one bin by listing the original categories that should be merged, separated by "+" signs.

For example, if you want to create two bins from categories "A", "B", "C", "D":

• Bin 1: "A+B"

• Bin 2: "C+D"

Examples

# Sample data
feature <- c("A", "B", "C", "D", "A", "B", "C", "D")
target <- c(1, 0, 1, 0, 1, 1, 0, 0)

# Define custom bins: (A,B) and (C,D)
cutpoints <- c("A+B", "C+D")

# Apply binning
result <- ob_cutpoints_cat(feature, target, cutpoints)

# View bin statistics
print(result$woebin)

# View WoE-transformed feature
print(result$woefeature)