Apply Weight of Evidence Transformations to New Data

Applies the binning and Weight of Evidence (WoE) transformations learned by obwoe to new data. This is the scoring function for deploying WoE-based models in production. For each feature, the function assigns observations to bins and maps them to their corresponding WoE values.

Usage

obwoe_apply(
  data,
  obj,
  suffix_bin = "_bin",
  suffix_woe = "_woe",
  keep_original = TRUE,
  na_woe = 0
)

Arguments

data

A data.frame containing the features to transform. Must include all features present in the obj results. The target column is optional; if present, it will be included in the output.

obj

An object of class "obwoe" returned by obwoe.

suffix_bin

Character string suffix for bin columns. Default is "_bin".

suffix_woe

Character string suffix for WoE columns. Default is "_woe".

keep_original

Logical. If TRUE (default), include the original feature columns in the output. If FALSE, only bin and WoE columns are returned.

na_woe

Numeric value to assign when an observation cannot be mapped to a bin (e.g., new categories not seen during training). Default is 0.

Value

A data.frame containing:

target

The target column (if present in data)

<feature>

Original feature values (if keep_original = TRUE)

<feature>_bin

Assigned bin label for each observation

<feature>_woe

Weight of Evidence value for the assigned bin

Details

Bin Assignment Logic

Numerical Features: Observations are assigned to bins based on cutpoints stored in the obwoe object. The cut() function is used with intervals \((a_i, a_{i+1}]\) where \(a_0 = -\infty\) and \(a_k = +\infty\).

Categorical Features: Categories are matched directly to bin labels. Categories not seen during training are assigned NA for bin and na_woe for WoE.

Production Deployment

For production scoring, it is recommended to:

1. Train the binning model using obwoe() on the training set

2. Save the fitted object with saveRDS()

3. Load and apply using obwoe_apply() on new data

The WoE-transformed features can be used directly as inputs to logistic regression or other linear models, enabling interpretable credit scorecards.

References

Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. John Wiley & Sons. doi:10.1002/9781119201731

See also

obwoe for fitting the binning model, summary.obwoe for model diagnostics.

Examples

# \donttest{
# =============================================================================
# Example 1: Basic Usage - Train and Apply
# =============================================================================
set.seed(42)
n <- 1000

# Training data
train_df <- data.frame(
  age = rnorm(n, 40, 15),
  income = exp(rnorm(n, 10, 0.8)),
  education = sample(c("HS", "BA", "MA", "PhD"), n, replace = TRUE),
  target = rbinom(n, 1, 0.15)
)

# Fit binning model
model <- obwoe(train_df, target = "target")

# New data for scoring (could be validation/test set)
new_df <- data.frame(
  age = c(25, 45, 65),
  income = c(20000, 50000, 80000),
  education = c("HS", "MA", "PhD")
)

# Apply transformations
scored <- obwoe_apply(new_df, model)
print(scored)
#>   age               age_bin     age_woe income                 income_bin
#> 1  25      (-Inf;39.802484] -0.07802808  20000 (8069.329816;61783.844034]
#> 2  45 (41.347493;52.361109]  0.03006841  50000 (8069.329816;61783.844034]
#> 3  65      (52.361109;+Inf]  0.13769907  80000        (61783.844034;+Inf]
#>    income_woe education education_bin education_woe
#> 1 -0.03653409        HS            HS   -0.43188518
#> 2 -0.03653409        MA            MA   -0.09570827
#> 3 -0.04630457       PhD           PhD    0.19851891

# Use WoE features for downstream modeling
woe_cols <- grep("_woe$", names(scored), value = TRUE)
print(woe_cols)
#> [1] "age_woe"       "income_woe"    "education_woe"

# =============================================================================
# Example 2: Without Original Features
# =============================================================================

scored_compact <- obwoe_apply(new_df, model, keep_original = FALSE)
print(scored_compact)
#>                 age_bin     age_woe                 income_bin  income_woe
#> 1      (-Inf;39.802484] -0.07802808 (8069.329816;61783.844034] -0.03653409
#> 2 (41.347493;52.361109]  0.03006841 (8069.329816;61783.844034] -0.03653409
#> 3      (52.361109;+Inf]  0.13769907        (61783.844034;+Inf] -0.04630457
#>   education_bin education_woe
#> 1            HS   -0.43188518
#> 2            MA   -0.09570827
#> 3           PhD    0.19851891
# }