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Binning Numerical Variables using Custom Cutpoints

Source: R/ob_binning_cutpoints_num.R
ob_cutpoints_num.Rd

This function applies user-defined binning to a numerical variable by using specified cutpoints to create intervals and calculates Weight of Evidence (WoE) and Information Value (IV) for each interval bin.

Usage

ob_cutpoints_num(feature, target, cutpoints)

Arguments

feature

A numeric vector representing the continuous predictor variable.

target

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

cutpoints

A numeric vector of cutpoints that define bin boundaries. These will be automatically sorted in ascending order.

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 interval notation (e.g., "(10.00;20.00]")

  • 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

cutpoints

The sorted numeric cutpoints, exposed at the top level so this result can be reassembled into ob_apply_woe_num's obresults argument (see Details)

id

Sequential bin identifier, same as woebin$id; duplicated at the top level for the same reason as cutpoints

Details

The function takes a numeric vector of cutpoints that define the boundaries between bins. For n cutpoints, n+1 bins are created, each right-closed – \((a, b]\) – matching ob_apply_woe_num's default (include_upper_bound = TRUE) and the convention obwoe/obwoe_apply use throughout the package:

  • Bin 1: \((-\infty, cutpoint_1]\)

  • Bin 2: \((cutpoint_1, cutpoint_2]\)

  • ...

  • Bin n+1: \((cutpoint_n, +\infty)\)

result does not have the exact shape ob_apply_woe_num expects as obresults (which needs top-level cutpoints, woe and id), because woebin also carries count/ iv columns. To round-trip, reassemble it: list(cutpoints = result$cutpoints, woe = result$woebin$woe, id = result$id, bin = result$woebin$bin). Before 1.13.1, result carried no id or top-level cutpoints at all (so a round trip through ob_apply_woe_num was not even possible without reconstructing them by hand), and the bin boundaries were left-closed \([a, b)\), the opposite of ob_apply_woe_num's include_upper_bound = TRUE default – a value that landed exactly on a cutpoint got a different, often sign-flipped, WoE depending on whether it went through the fit or the apply side.

Note

  • Target variable must contain only 0 and 1 values.

  • Cutpoints are sorted automatically in ascending order.

  • Interval notation uses "(" for exclusive and "]" for inclusive bounds – a value exactly equal to a cutpoint falls in the bin that ends at that cutpoint.

  • Infinite values in feature are handled appropriately.

Examples

# Sample data
feature <- c(5, 15, 25, 35, 45, 55, 65, 75)
target <- c(0, 0, 1, 1, 1, 1, 0, 0)

# Define custom cutpoints
cutpoints <- c(30, 60)

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

# View bin statistics
print(result$woebin)
#>   id           bin count count_pos count_neg        woe        iv
#> 1  1  (-Inf;30.00]     3         1         2 -0.6931472 0.1732868
#> 2  2 (30.00;60.00]     3         3         0  8.9226583 6.6911015
#> 3  3  (60.00;+Inf]     2         0         2 -8.5171932 4.2577449

# View WoE-transformed feature
print(result$woefeature)
#> [1] -0.6931472 -0.6931472 -0.6931472  8.9226583  8.9226583  8.9226583 -8.5171932
#> [8] -8.5171932

# Round-trip through ob_apply_woe_num() with its defaults
apply_obj <- list(
  cutpoints = result$cutpoints, woe = result$woebin$woe,
  id = result$id, bin = result$woebin$bin
)
woe_new <- ob_apply_woe_num(apply_obj, feature)
stopifnot(isTRUE(all.equal(woe_new$woe, result$woefeature)))