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Binning Categorical Variables using Custom Cutpoints

Source: R/ob_binning_cutpoints_cat.R
ob_cutpoints_cat.Rd

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)

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.

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:

  • id: Sequential bin identifier

  • bin: The bin definition – original categories joined by "%;%" (not the "+" used in the cutpoints input; see Details), matching the separator ob_apply_woe_cat defaults to and every ob_categorical_*() algorithm in the main pipeline emits

  • 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

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"

cutpoints still uses "+" as the input separator (simple to type, e.g. "A+B"), but result$woebin is built so it can be handed straight back to ob_apply_woe_cat with its defaults – ob_apply_woe_cat(result$woebin, new_feature) – and get the same WoE this function itself assigned. Before 1.13.1 the emitted bin labels echoed the "+"-joined input verbatim and carried no id column, so a round trip through ob_apply_woe_cat's default bin_separator = "%;%" matched no category and silently fell back to a "Special"/NA bin for every observation.

Note

  • Target variable must contain only 0 and 1 values.

  • Every unique category in feature must be included in exactly one bin definition in cutpoints.

  • Categories not mentioned in cutpoints will be assigned to bin 0 (which may lead to unexpected results).

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)
#>   id   bin count count_pos count_neg       woe        iv
#> 1  1 A%;%B     4         3         1  1.098612 0.5493061
#> 2  2 C%;%D     4         1         3 -1.098612 0.5493061

# View WoE-transformed feature
print(result$woefeature)
#> [1]  1.098612  1.098612 -1.098612 -1.098612  1.098612  1.098612 -1.098612
#> [8] -1.098612

# Round-trip through ob_apply_woe_cat() with its defaults
woe_new <- ob_apply_woe_cat(result$woebin, feature)
stopifnot(isTRUE(all.equal(woe_new$woe, result$woefeature)))