OptimalBinningWoE (version 1.0.8)

ob_cutpoints_num: Binning Numerical Variables using Custom Cutpoints

Description

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)

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 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

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.

Details

The function takes a numeric vector of cutpoints that define the boundaries between bins. For n cutpoints, n+1 bins are created:

• Bin 1: \((-\infty, cutpoint_1)\)

• Bin 2: \([cutpoint_1, cutpoint_2)\)

• ...

• Bin n+1: \([cutpoint_n, +\infty)\)

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)

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