OptimalBinningWoE (version 1.0.8)

ob_apply_woe_cat: Apply Optimal Weight of Evidence (WoE) to a Categorical Feature

Description

Transforms a categorical feature into its corresponding Weight of Evidence (WoE) values using pre-computed binning results from an optimal binning algorithm (e.g., ob_categorical_cm).

Usage

ob_apply_woe_cat(
  obresults,
  feature,
  bin_separator = "%;%",
  missing_values = c("NA", "Missing", "")
)

Value

Numeric vector of WoE values with the same length as feature. Categories not found in obresults will produce NA values with a warning.

Arguments

obresults

List output from an optimal binning function for categorical variables. Must contain elements bin (character vector of bin labels) and woe (numeric vector of WoE values). Bins may represent individual categories or merged groups separated by bin_separator.

feature

Character or factor vector of categorical values to be transformed. Automatically coerced to character if provided as factor.

bin_separator

Character string used to separate multiple categories within a single bin label (default: "%;%"). For example, a bin "A%;%B%;%C" contains categories A, B, and C.

missing_values

Character vector specifying which values should be treated as missing (default: c("NA", "Missing", "")). These values are matched against a special bin labeled "NA" or "Missing" in obresults.

Details

This function is typically used in a two-step workflow:

1. Train binning on training data: bins <- ob_categorical_cm(feature_train, target_train)

2. Apply WoE to new data: woe_test <- ob_apply_woe_cat(bins, feature_test)

The function performs exact string matching between categories in feature and the bin labels in obresults$bin. For merged bins (containing bin_separator), the string is split and each component is matched individually.

Examples

# \donttest{
# Mock data
train_data <- data.frame(
  category = c("A", "B", "A", "C", "B", "A"),
  default = c(0, 1, 0, 1, 0, 0)
)
test_data <- data.frame(
  category = c("A", "C", "B")
)

# Train binning on training set
train_bins <- ob_categorical_cm(
  feature = train_data$category,
  target = train_data$default
)

# Apply to test set
test_woe <- ob_apply_woe_cat(
  obresults = train_bins,
  feature = test_data$category
)

# Handle custom missing indicators
test_woe <- ob_apply_woe_cat(
  obresults = train_bins,
  feature = test_data$category,
  missing_values = c("NA", "Unknown", "N/A", "")
)
# }