Apply Optimal Weight of Evidence (WoE) to a Numerical Feature

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

Usage

ob_apply_woe_num(
  obresults,
  feature,
  include_upper_bound = TRUE,
  missing_values = c(-999)
)

Arguments

obresults

List output from an optimal binning function for numerical variables. Must contain elements cutpoints (numeric vector of bin boundaries) and woe (numeric vector of WoE values). The number of WoE values should equal length(cutpoints) + 1.

feature

Numeric vector of values to be transformed. Automatically coerced to numeric if provided in another type.

include_upper_bound

Logical flag controlling interval boundary behavior (default: TRUE):

• TRUE: Intervals are (lower, upper] (right-closed).

• FALSE: Intervals are [lower, upper) (left-closed).

This must match the convention used during binning.

missing_values

Numeric vector of values to be treated as missing (default: c(-999)). These values are assigned the WoE of the special missing bin if it exists in obresults, or NA otherwise.

Value

A data frame with one row per element of feature and the columns feature, bin (interval label, or "Special"), woe, idbin and ismissing. Values outside the range of cutpoints are assigned to the first or last bin. NA/NaN values and values listed in missing_values get ismissing = 1 and the WoE of the missing-value bin when obresults has one (see Details), or bin = "Special" and woe = NA otherwise.

Details

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

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

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

Bin Assignment Logic: For k cutpoints \(c_1 < c_2 < \cdots < c_k\), values are assigned as:

• Bin 1: \(x \le c_1\) (if include_upper_bound = TRUE)

• Bin i: \(c_{i-1} < x \le c_i\) for \(i = 2, \ldots, k\)

• Bin k+1: \(x > c_k\)

Handling of Edge Cases:

• obresults$woe and obresults$id may carry length(cutpoints) + 2 entries when the fit has a dedicated missing-value bin (e.g. ob_numerical_udt on data with NA): that bin is the one labelled "NA" or "Missing" in obresults$bin (the last one when no label says so), and missing values are scored with it.

• With no cut points at all (a single bin) every non-missing value gets the WoE of that bin.

• Each value is placed with one binary search over the cut points, \(O(n \log k)\) in total.

• Inf and -Inf are assigned to the last and first bins, respectively.

• Values exactly equal to cutpoints follow the include_upper_bound convention.

See also

ob_numerical_mdlp for MDLP binning, ob_numerical_mob for monotonic binning, ob_apply_woe_cat for applying WoE to categorical features.

Examples

# \donttest{
# Mock data
train_data <- data.frame(
  income = c(50000, 75000, 30000, 45000, 80000, 60000),
  default = c(0, 0, 1, 1, 0, 0)
)
test_data <- data.frame(
  income = c(55000, 35000, 90000)
)

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

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

# Handle custom missing indicators (e.g., -999, -1)
test_woe <- ob_apply_woe_num(
  obresults = train_bins,
  feature = test_data$income,
  missing_values = c(-999, -1, -9999)
)

# Use left-closed intervals (match scikit-learn convention)
test_woe <- ob_apply_woe_num(
  obresults = train_bins,
  feature = test_data$income,
  include_upper_bound = FALSE
)
# }