Author: José Evandeilton Lopes
Date: 2026-01-28

# OptimalBinningWoE: Practical Guide for Credit Risk Modeling

#### José Evandeilton Lopes

#### 2026-01-28

# Introduction

OptimalBinningWoE implements 36 high-performance binning algorithms for Weight of Evidence (WoE) transformation in credit scoring and risk modeling. This vignette demonstrates practical applications using real-world credit data.

## Package Overview

The package provides:

- 36 algorithms: 20 numerical + 16 categorical methods
- C++ performance: Fast processing of large datasets
- tidymodels integration: Production-ready ML pipelines
- Regulatory compliance: Monotonic binning for Basel/IFRS 9
- Comprehensive metrics: IV, KS, Gini, lift curves

## Theoretical Foundation

### Weight of Evidence (WoE)

For bin \(i\), WoE quantifies the logarithmic odds ratio:

\[\text{WoE}_i = \ln\left(\frac{\text{Distribution of Events}_i}{\text{Distribution of Non-Events}_i}\right) = \ln\left(\frac{n_{i,1}/N_1}{n_{i,0}/N_0}\right)\]

Interpretation: - WoE > 0: Higher risk than population average - WoE < 0: Lower risk than population average
- WoE ≈ 0: Similar to population average

### Information Value (IV)

IV measures total predictive power:

\[\text{IV} = \sum_{i=1}^{k} \left(\frac{n_{i,1}}{N_1} - \frac{n_{i,0}}{N_0}\right) \times \text{WoE}_i\]

Benchmarks (Siddiqi, 2006):

IV Range | Predictive Power | Recommendation
--- | --- | ---
< 0.02 | Unpredictive | Exclude
0.02 - 0.10 | Weak | Marginal
0.10 - 0.30 | Medium | Include
0.30 - 0.50 | Strong | Prioritize
> 0.50 | Suspicious | Check leakage

# Installation

    # From GitHub
    devtools::install_github("evandeilton/OptimalBinningWoE")

    # Install dependencies for this vignette
    install.packages(c("scorecard", "tidymodels", "pROC"))

# Dataset: German Credit Data

    library(OptimalBinningWoE)
    library(scorecard)

    # Load German credit dataset
    data("germancredit", package = "scorecard")

    # Inspect structure
    dim(germancredit)
    #> [1] 1000   21
    str(germancredit[, 1:8])
    #> 'data.frame':    1000 obs. of  8 variables:
    #>  $ status.of.existing.checking.account                : Factor w/ 4 levels "... < 0 DM","0 <= ... < 200 DM",..: 1 2 4 1 1 4 4 2 4 2 ...
    #>  $ duration.in.month                                  : num  6 48 12 42 24 36 24 36 12 30 ...
    #>  $ credit.history                                     : Factor w/ 5 levels "no credits taken/ all credits paid back duly",..: 5 3 5 3 4 3 3 3 3 5 ...
    #>  $ purpose                                            : chr  "radio/television" "radio/television" "education" "furniture/equipment" ...
    #>  $ credit.amount                                      : num  1169 5951 2096 7882 4870 ...
    #>  $ savings.account.and.bonds                          : Factor w/ 5 levels "... < 100 DM",..: 5 1 1 1 1 5 3 1 4 1 ...
    #>  $ present.employment.since                           : Factor w/ 5 levels "unemployed","... < 1 year",..: 5 3 4 4 3 3 5 3 4 1 ...
    #>  $ installment.rate.in.percentage.of.disposable.income: num  4 2 2 2 3 2 3 2 2 4 ...

    # Target variable
    table(germancredit$creditability)
    #>
    #>  bad good
    #>  300  700
    cat("\nDefault rate:", round(mean(germancredit$creditability == "bad") * 100, 2), "%\n")
    #>
    #> Default rate: 30 %

## Data Preparation

    # Create binary target (must be a factor for tidymodels classification)
    german <- germancredit
    german$default <- factor(
      ifelse(german$creditability == "bad", 1, 0),
      levels = c(0, 1),
      labels = c("good", "bad")
    )
    german$creditability <- NULL

    # Select key features for demonstration
    features_num <- c("duration.in.month", "credit.amount", "age.in.years")
    features_cat <- c(
      "status.of.existing.checking.account", "credit.history",
      "purpose", "savings.account.and.bonds"
    )

    german_model <- german[c("default", features_num, features_cat)]

    # Summary statistics
    cat("Numerical features:\n")
    #> Numerical features:
    summary(german_model[, features_num])
    #>  duration.in.month credit.amount    age.in.years
    #>  Min.   : 4.0      Min.   :  250   Min.   :19.00
    #>  1st Qu.:12.0      1st Qu.: 1366   1st Qu.:27.00
    #>  Median :18.0      Median : 2320   Median :33.00
    #>  Mean   :20.9      Mean   : 3271   Mean   :35.55
    #>  3rd Qu.:24.0      3rd Qu.: 3972   3rd Qu.:42.00
    #>  Max.   :72.0      Max.   :18424   Max.   :75.00

    cat("\n\nCategorical features:\n")
    #>
    #>
    #> Categorical features:
    sapply(german_model[, features_cat], function(x) length(unique(x)))
    #> status.of.existing.checking.account                      credit.history
    #>                                   4                                   5
    #>                             purpose           savings.account.and.bonds
    #>                                  10                                   5

# Quick Start: Single Feature Binning

    # Bin credit amount with JEDI algorithm
    result_single <- obwoe(
      data = german_model,
      target = "default",
      feature = "credit.amount",
      algorithm = "jedi",
      min_bins = 3,
      max_bins = 6
    )

    # View results
    print(result_single)
    #> Optimal Binning Weight of Evidence
    #> ===================================
    #>
    #> Target: default ( binary )
    #> Features processed: 1
    #>
    #> Results:  1  successful
    #>
    #> Top features by IV:
    #>   credit.amount: IV = 0.0023 (3 bins, jedi)

    # Detailed binning table
    result_single$results$credit.amount
    #> $id
    #> [1] 1 2 3
    #>
    #> $bin
    #> [1] "(-Inf;1283.000000]"        "(1283.000000;1393.000000]"
    #> [3] "(1393.000000;+Inf]"
    #>
    #> $woe
    #> [1]  0.07525674 -0.13353139 -0.01237398
    #>
    #> $iv
    #> [1] 0.0012184424 0.0009537957 0.0001119550
    #>
    #> $count
    #> [1] 212  55 733
    #>
    #> $count_pos
    #> [1]  67  15 218
    #>
    #> $count_neg
    #> [1] 145  40 515
    #>
    #> $cutpoints
    #> [1] 1283 1393
    #>
    #> $converged
    #> [1] TRUE
    #>
    #> $iterations
    #> [1] 2
    #>
    #> $feature
    #> [1] "credit.amount"
    #>
    #> $type
    #> [1] "numerical"
    #>
    #> $algorithm
    #> [1] "jedi"

## Visualize Binning Results

    # WoE pattern visualization
    plot(result_single, type = "woe")

## Key Insights from Single Feature

    # Extract metrics
    bins <- result_single$results$credit.amount

    cat("Binning Summary:\n")
    #> Binning Summary:
    cat("  Number of bins:", nrow(bins), "\n")
    #>   Number of bins:
    cat("  Total IV:", round(sum(bins$iv), 4), "\n")
    #>   Total IV: 0.0023
    cat("  Monotonic:", all(diff(bins$woe) >= 0) || all(diff(bins$woe) <= 0), "\n\n")
    #>   Monotonic: FALSE

    # Event rates by bin
    bins_summary <- data.frame(
      Bin = bins$bin,
      Count = bins$count,
      Event_Rate = round(bins$count_pos / bins$count * 100, 2),
      WoE = round(bins$woe, 4),
      IV_Contribution = round(bins$iv, 4)
    )

    print(bins_summary)
    #>                         Bin Count Event_Rate     WoE IV_Contribution
    #> 1        (-Inf;1283.000000]   212      31.60  0.0753          0.0012
    #> 2 (1283.000000;1393.000000]    55      27.27 -0.1335          0.0010
    #> 3        (1393.000000;+Inf]   733      29.74 -0.0124          0.0001

# Multiple Features: Automated Binning

    # Bin all features simultaneously
    result_multi <- obwoe(
      data = german_model,
      target = "default",
      algorithm = "cm",
      min_bins = 3,
      max_bins = 4
    )

    # Summary of all features
    summary(result_multi)
    #> Summary: Optimal Binning Weight of Evidence
    #> ============================================
    #>
    #> Target: default ( binary )
    #>
    #> Aggregate Statistics:
    #>   Features: 7 total, 7 successful, 0 errors
    #>   Total IV: 1.5179
    #>   Mean IV: 0.2168 (SD: 0.2231)
    #>   Median IV: 0.1897
    #>   IV Range: [0.0018, 0.6624]
    #>   Mean Bins: 3.6
    #>
    #> IV Classification (Siddiqi, 2006):
    #>   Unpredictive: 2 features
    #>   Medium      : 4 features
    #>   Suspicious  : 1 features
    #>
    #> Feature Details:
    #>                              feature        type n_bins    total_iv
    #>  status.of.existing.checking.account categorical      4 0.662430485
    #>                       credit.history categorical      4 0.289999923
    #>                    duration.in.month   numerical      4 0.206606852
    #>            savings.account.and.bonds categorical      3 0.189741384
    #>                              purpose categorical      4 0.161473063
    #>                        credit.amount   numerical      3 0.005917450
    #>                         age.in.years   numerical      3 0.001775917
    #>      iv_class
    #>    Suspicious
    #>        Medium
    #>        Medium
    #>        Medium
    #>        Medium
    #>  Unpredictive
    #>  Unpredictive

## Feature Selection by IV

    # Extract IV summary
    iv_summary <- result_multi$summary[!result_multi$summary$error, ]
    iv_summary <- iv_summary[order(-iv_summary$total_iv), ]

    # Top predictive features
    cat("Top 5 Features by Information Value:\n\n")
    #> Top 5 Features by Information Value:
    print(head(iv_summary[, c("feature", "total_iv", "n_bins")], 5))
    #>                               feature  total_iv n_bins
    #> 4 status.of.existing.checking.account 0.6624305      4
    #> 5                      credit.history 0.2899999      4
    #> 1                   duration.in.month 0.2066069      4
    #> 7           savings.account.and.bonds 0.1897414      3
    #> 6                             purpose 0.1614731      4

    # Select features with IV >= 0.02
    strong_features <- iv_summary$feature[iv_summary$total_iv >= 0.02]
    cat("\n\nFeatures with IV >= 0.02:", length(strong_features), "\n")
    #>
    #>
    #> Features with IV >= 0.02: 5

## Gains Table Analysis

    # Compute gains for best numerical feature
    best_num_feature <- iv_summary$feature[
      iv_summary$feature %in% features_num
    ][1]

    gains <- obwoe_gains(result_multi, feature = best_num_feature, sort_by = "id")

    print(gains)
    #> Gains Table: duration.in.month
    #> ==================================================
    #>
    #> Observations: 487  |  Bins: 4
    #> Total IV: 0.4220
    #>
    #> Performance Metrics:
    #>   KS Statistic: 27.55%
    #>   Gini Coefficient: 34.25%
    #>   AUC: 0.3287
    #>
    #>                    bin count pos_rate     woe     iv cum_pos_pct    ks lift
    #>        (-Inf;6.000000]    50   10.00% -1.2676 0.1270        3.3% 10.0% 0.32
    #>   (7.000000;15.000000]   150   18.67% -0.6128 0.1075       22.0% 27.6% 0.32
    #>  (16.000000;36.000000]   200   36.00%  0.2731 0.0274       70.0% 17.5% 0.32
    #>       (39.000000;+Inf]    87   51.72%  0.9136 0.1602      100.0%  0.0% 0.32

    # Plot gains curves
    oldpar <- par(mfrow = c(2, 2))
    plot(gains, type = "cumulative")
    plot(gains, type = "ks")
    plot(gains, type = "lift")
    plot(gains, type = "woe_iv")

    par(oldpar)

# Algorithm Comparison

Different algorithms excel in different scenarios. Let’s compare performance.

    # Test multiple algorithms on credit.amount
    algorithms <- c("jedi", "mdlp", "mob", "ewb", "cm")

    compare_algos <- function(data, target, feature, algos) {
      results <- lapply(algos, function(algo) {
        tryCatch(
          {
            fit <- obwoe(
              data = data,
              target = target,
              feature = feature,
              algorithm = algo,
              min_bins = 3,
              max_bins = 6
            )

            data.frame(
              Algorithm = algo,
              N_Bins = fit$summary$n_bins[1],
              IV = round(fit$summary$total_iv[1], 4),
              Converged = fit$summary$converged[1],
              stringsAsFactors = FALSE
            )
          },
          error = function(e) {
            # Return NA but log error for debugging during vignette rendering
            message(sprintf("Algorithm '%s' failed: %s", algo, e$message))
            data.frame(
              Algorithm = algo,
              N_Bins = NA_integer_,
              IV = NA_real_,
              Converged = FALSE,
              stringsAsFactors = FALSE
            )
          }
        )
      })

      do.call(rbind, results)
    }

    # Compare on credit.amount
    comp_result <- compare_algos(
      german_model,
      "default",
      "credit.amount",
      algorithms
    )

    cat("Algorithm Comparison on 'credit.amount':\n\n")
    #> Algorithm Comparison on 'credit.amount':
    print(comp_result[order(-comp_result$IV), ])
    #>   Algorithm N_Bins     IV Converged
    #> 3       mob      5 0.0917      TRUE
    #> 4       ewb      3 0.0735      TRUE
    #> 5        cm      3 0.0617      TRUE
    #> 2      mdlp      3 0.0107      TRUE
    #> 1      jedi      3 0.0023      TRUE

## Algorithm Selection Guide

    # View algorithm capabilities
    algo_info <- obwoe_algorithms()

    cat("Algorithm Categories:\n\n")
    #> Algorithm Categories:

    cat("Fast for Large Data (O(n) complexity):\n")
    #> Fast for Large Data (O(n) complexity):
    print(algo_info[
      algo_info$algorithm %in% c("ewb", "sketch"),
      c("algorithm", "numerical", "categorical")
    ])
    #>    algorithm numerical categorical
    #> 8     sketch      TRUE        TRUE
    #> 18       ewb      TRUE       FALSE

    cat("\n\nRegulatory Compliant (Monotonic):\n")
    #>
    #>
    #> Regulatory Compliant (Monotonic):
    print(algo_info[
      algo_info$algorithm %in% c("mob", "mblp", "ir"),
      c("algorithm", "numerical", "categorical")
    ])
    #>    algorithm numerical categorical
    #> 7        mob      TRUE        TRUE
    #> 20        ir      TRUE       FALSE
    #> 24      mblp      TRUE       FALSE

    cat("\n\nGeneral Purpose (algorithm):\n")
    #>
    #>
    #> General Purpose (algorithm):
    print(algo_info[
      algo_info$name %in% c("jedi", "cm", "mdlp"),
      c("algorithm", "numerical", "categorical")
    ])
    #> [1] algorithm   numerical   categorical
    #> <0 linhas> (ou row.names de comprimento 0)

## Algorithm Selection by Use Case

Use Case | Recommended Algorithm | Rationale
--- | --- | ---
General credit scoring | `jedi`, `mob` | Best balance of speed and predictive power.
Monotonicity mandatory | `mob`, `mblp`, `ir` | Guaranteed monotonic WoE profile for regulatory compliance.
Large datasets (>1M rows) | `sketch`, `ewb` | Constant or sublinear memory footprint.
Non-linear associations | `dp`, `cm` | Optimal partitioning (Dynamic Programming) capturing complex trends.
Mixed data types | `jedi_mwoe`, `udt` | Handles both numerical and categorical features uniformly.
Outlier robustness | `mdlp`, `fetb` | Entropy-based discretization less sensitive to extreme values.
Sparse categorical data | `gmb`, `ivb`, `swb` | Groups infrequent categories based on similar risk profiles.

### Complete Algorithm List (36 Algorithms)

Type | Algorithms
--- | ---
Universal (9) | `jedi`, `jedi_mwoe`, `cm`, `dp`, `dmiv`, `fetb`, `mob`, `sketch`, `udt`
Numerical (12) | `bb`, `ewb`, `fast_mdlp`, `ir`, `kmb`, `ldb`, `lpdb`, `mblp`, `mdlp`, `mrblp`, `oslp`, `ubsd`
Categorical (15) | `gmb`, `ivb`, `mba`, `milp`, `sab`, `sblp`, `swb` (and others)

Full mapping can be inspected via `obwoe_algorithms()`.

# Production Pipeline with tidymodels

The most powerful application is integrating WoE into production ML workflows.

    library(tidymodels)

    # Train/test split with stratification
    set.seed(123)
    german_split <- initial_split(german_model, prop = 0.7, strata = default)
    train_data <- training(german_split)
    test_data <- testing(german_split)

    cat("Training set:", nrow(train_data), "observations\n")
    #> Training set: 699 observations
    cat("Test set:", nrow(test_data), "observations\n")
    #> Test set: 301 observations
    cat("Train default rate:", round(mean(train_data$default == "bad") * 100, 2), "%\n")
    #> Train default rate: 30.04 %

## Define Preprocessing Recipe

    # Create recipe with WoE transformation
    rec_woe <- recipe(default ~ ., data = train_data) %>%
      step_obwoe(
        all_predictors(),
        outcome = "default",
        algorithm = "jedi",
        min_bins = 2,
        max_bins = tune(), # Hyperparameter tuning
        bin_cutoff = 0.05,
        output = "woe"
      )

    # Preview recipe
    rec_woe
    #> Optimal Binning WoE (all_predictors()) [algorithm='jedi']

## Model Specification and Workflow

    # Logistic regression specification
    lr_spec <- logistic_reg() %>%
      set_engine("glm") %>%
      set_mode("classification")

    # Create complete workflow
    wf_credit <- workflow() %>%
      add_recipe(rec_woe) %>%
      add_model(lr_spec)

    wf_credit
    #> ══ Workflow ════════════════════════════════════════════════════════════════════
    #> Preprocessor: Recipe
    #> Model: logistic_reg()
    #>
    #> ── Preprocessor ────────────────────────────────────────────────────────────────
    #> 1 Recipe Step
    #>
    #> • step_obwoe()
    #>
    #> ── Model ───────────────────────────────────────────────────────────────────────
    #> Logistic Regression Model Specification (classification)
    #>
    #> Computational engine: glm

## Hyperparameter Tuning

    # Define tuning grid
    tune_grid <- tibble(max_bins = c(4, 6, 8))

    # Create cross-validation folds
    set.seed(456)
    cv_folds <- vfold_cv(train_data, v = 5, strata = default)

    # Tune workflow
    tune_results <- tune_grid(
      wf_credit,
      resamples = cv_folds,
      grid = tune_grid,
      metrics = metric_set(roc_auc, accuracy)
    )

    # Best configuration
    collect_metrics(tune_results) %>%
      # filter(.metric == "roc_auc") %>%
      arrange(desc(mean))
    #> # A tibble: 6 × 7
    #>   max_bins .metric  .estimator  mean     n std_err .config
    #>      <dbl> <chr>    <chr>      <dbl> <int>   <dbl> <chr>
    #> 1        4 roc_auc  binary     0.783     5 0.0168  pre1_mod0_post0
    #> 2        6 roc_auc  binary     0.783     5 0.0176  pre2_mod0_post0
    #> 3        8 roc_auc  binary     0.782     5 0.0177  pre3_mod0_post0
    #> 4        6 accuracy binary     0.752     5 0.00955 pre2_mod0_post0
    #> 5        8 accuracy binary     0.751     5 0.00974 pre3_mod0_post0
    #> 6        4 accuracy binary     0.745     5 0.00854 pre1_mod0_post0

    # Visualize tuning
    autoplot(tune_results, metric = "roc_auc")

## Final Model Fitting

    # Select best parameters
    best_params <- select_best(tune_results, metric = "roc_auc")
    cat("Optimal max_bins:", best_params$max_bins, "\n\n")
    #> Optimal max_bins: 4

    # Finalize and fit
    final_wf <- finalize_workflow(wf_credit, best_params)
    final_fit <- fit(final_wf, data = train_data)

    # Extract coefficients
    final_fit %>%
      extract_fit_parsnip() %>%
      tidy() %>%
      arrange(desc(abs(estimate)))
    #> # A tibble: 8 × 5
    #>   term                                estimate std.error statistic  p.value
    #>   <chr>                                  <dbl>     <dbl>     <dbl>    <dbl>
    #> 1 purpose                                1.00      0.233      4.29 1.76e- 5
    #> 2 savings.account.and.bonds              0.911     0.236      3.86 1.12e- 4
    #> 3 (Intercept)                           -0.830     0.100     -8.29 1.13e-16
    #> 4 age.in.years                           0.824     0.289      2.85 4.36e- 3
    #> 5 status.of.existing.checking.account    0.811     0.117      6.93 4.22e-12
    #> 6 credit.history                         0.748     0.167      4.48 7.63e- 6
    #> 7 credit.amount                          0.726     0.312      2.33 1.99e- 2
    #> 8 duration.in.month                      0.704     0.200      3.51 4.42e- 4

## Model Evaluation

    # Predictions on test set
    test_pred <- augment(final_fit, test_data)

    # Performance metrics
    metrics <- metric_set(roc_auc, accuracy, sens, spec, precision)
    metrics(test_pred,
      truth = default, estimate = .pred_class,
      .pred_bad, event_level = "second"
    )
    #> # A tibble: 5 × 3
    #>   .metric   .estimator .estimate
    #>   <chr>     <chr>          <dbl>
    #> 1 accuracy  binary         0.681
    #> 2 sens      binary         0.344
    #> 3 spec      binary         0.825
    #> 4 precision binary         0.456
    #> 5 roc_auc   binary         0.732

    # ROC curve
    roc_curve(test_pred,
      truth = default, .pred_bad,
      event_level = "second"
    ) %>%
      autoplot() +
      labs(title = "ROC Curve - German Credit Model")

## Inspect Learned Binning Rules

    # Extract trained recipe
    trained_rec <- extract_recipe(final_fit)
    woe_step <- trained_rec$steps[[1]]

    # View binning for credit.amount
    credit_bins <- woe_step$binning_results$credit.amount

    data.frame(
      Bin = credit_bins$bin,
      WoE = round(credit_bins$woe, 4),
      IV = round(credit_bins$iv, 4)
    )
    #>                         Bin     WoE     IV
    #> 1         (-Inf;700.000000] -0.2156 0.0022
    #> 2  (700.000000;3249.000000] -0.2041 0.0244
    #> 3 (3249.000000;5954.000000]  0.0396 0.0003
    #> 4        (5954.000000;+Inf]  0.7652 0.0935

# Traditional Scorecard Development

For traditional credit scorecards outside tidymodels.

## Train-Test Split

    set.seed(789)
    n_total <- nrow(german_model)
    train_idx <- sample(1:n_total, size = 0.7 * n_total)

    train_sc <- german_model[train_idx, ]
    test_sc <- german_model[-train_idx, ]

## Fit Optimal Binning

    # Use monotonic binning for regulatory compliance
    sc_binning <- obwoe(
      data = train_sc,
      target = "default",
      algorithm = "mob", # Monotonic Optimal Binning
      min_bins = 3,
      max_bins = 5,
      control = control.obwoe(
        bin_cutoff = 0.05,
        convergence_threshold = 1e-6
      )
    )

    summary(sc_binning)
    #> Summary: Optimal Binning Weight of Evidence
    #> ============================================
    #>
    #> Target: default ( binary )
    #>
    #> Aggregate Statistics:
    #>   Features: 7 total, 7 successful, 0 errors
    #>   Total IV: 1.8858
    #>   Mean IV: 0.2694 (SD: 0.3355)
    #>   Median IV: 0.1432
    #>   IV Range: [0.0005, 0.9570]
    #>   Mean Bins: 3.7
    #>
    #> IV Classification (Siddiqi, 2006):
    #>   Unpredictive: 2 features
    #>   Weak        : 1 features
    #>   Medium      : 2 features
    #>   Strong      : 1 features
    #>   Suspicious  : 1 features
    #>
    #> Feature Details:
    #>                              feature        type n_bins    total_iv
    #>  status.of.existing.checking.account categorical      4 0.956958967
    #>            savings.account.and.bonds categorical      5 0.411933550
    #>                       credit.history categorical      5 0.260486195
    #>                    duration.in.month   numerical      3 0.143223080
    #>                              purpose categorical      3 0.095403913
    #>                         age.in.years   numerical      3 0.017231668
    #>                        credit.amount   numerical      3 0.000523977
    #>      iv_class
    #>    Suspicious
    #>        Strong
    #>        Medium
    #>        Medium
    #>          Weak
    #>  Unpredictive
    #>  Unpredictive

## Apply WoE Transformation

    # Transform training data with error handling
    train_woe <- tryCatch(
      {
        obwoe_apply(train_sc, sc_binning, keep_original = FALSE)
      },
      error = function(e) {
        message("Error in obwoe_apply for training data: ", e$message)
        message("This may occur with certain data distributions. Skipping transformation.")
        return(NULL)
      }
    )

    # Only proceed if transformation succeeded
    if (!is.null(train_woe)) {
      # Transform test data (uses training bins)
      test_woe <- obwoe_apply(test_sc, sc_binning, keep_original = FALSE)

      # Preview transformed features
      head(train_woe[, c("default", grep("_woe$", names(train_woe), value = TRUE)[1:3])], 10)
    } else {
      message("Skipping WoE transformation demonstration due to data incompatibility.")
    }
    #>    default duration.in.month_woe credit.amount_woe age.in.years_woe
    #> 1      bad             0.1024181      -0.002787588      -0.04548908
    #> 2     good             0.1024181      -0.002787588      -0.04548908
    #> 3     good             0.1024181       0.054981244      -0.04548908
    #> 4     good             0.1024181      -0.002787588      -0.04548908
    #> 5     good             0.1024181      -0.002787588      -0.04548908
    #> 6     good             0.1024181      -0.002787588      -0.04548908
    #> 7     good             0.1024181      -0.002787588       0.31220411
    #> 8     good             0.1024181       0.054981244      -0.04548908
    #> 9     good             0.1024181      -0.002787588      -0.04548908
    #> 10    good             0.1024181      -0.002787588      -0.04548908

## Build Logistic Regression

    if (!is.null(train_woe)) {
      # Select features with IV >= 0.02
      selected <- sc_binning$summary$feature[
        sc_binning$summary$total_iv >= 0.02 &
          !sc_binning$summary$error
      ]

      woe_vars <- paste0(selected, "_woe")
      formula_str <- paste("default ~", paste(woe_vars, collapse = " + "))

      # Fit model
      scorecard_glm <- glm(
        as.formula(formula_str),
        data = train_woe,
        family = binomial(link = "logit")
      )

      summary(scorecard_glm)
    } else {
      message("Skipping model building - WoE transformation failed.")
    }
    #>
    #> Call:
    #> glm(formula = as.formula(formula_str), family = binomial(link = "logit"),
    #>     data = train_woe)
    #>
    #> Coefficients:
    #>                                         Estimate Std. Error z value Pr(>|z|)
    #> (Intercept)                             -0.79374    0.09883  -8.031 9.65e-16
    #> duration.in.month_woe                    0.72740    0.23363   3.114 0.001849
    #> status.of.existing.checking.account_woe  0.89237    0.10413   8.570  < 2e-16
    #> credit.history_woe                       0.74937    0.19002   3.944 8.03e-05
    #> purpose_woe                              1.02008    0.30818   3.310 0.000933
    #> savings.account.and.bonds_woe            0.75047    0.19641   3.821 0.000133
    #>
    #> (Intercept)                             ***
    #> duration.in.month_woe                   **
    #> status.of.existing.checking.account_woe ***
    #> credit.history_woe                      ***
    #> purpose_woe                             ***
    #> savings.account.and.bonds_woe           ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> (Dispersion parameter for binomial family taken to be 1)
    #>
    #>     Null deviance: 860.23  on 699  degrees of freedom
    #> Residual deviance: 665.59  on 694  degrees of freedom
    #> AIC: 677.59
    #>
    #> Number of Fisher Scoring iterations: 5

## Scorecard Validation

    if (!is.null(train_woe) && exists("scorecard_glm")) {
      library(pROC)

      # Predictions
      test_woe$score <- predict(scorecard_glm, newdata = test_woe, type = "response")

      # ROC curve
      roc_obj <- roc(test_woe$default, test_woe$score, quiet = TRUE)
      auc_val <- auc(roc_obj)

      # KS statistic
      ks_stat <- max(abs(
        ecdf(test_woe$score[test_woe$default == "bad"])(seq(0, 1, 0.01)) -
          ecdf(test_woe$score[test_woe$default == "good"])(seq(0, 1, 0.01))
      ))

      # Gini coefficient
      gini <- 2 * auc_val - 1

      cat("Scorecard Performance:\n")
      cat("  AUC:  ", round(auc_val, 4), "\n")
      cat("  Gini: ", round(gini, 4), "\n")
      cat("  KS:   ", round(ks_stat * 100, 2), "%\n")

      # ROC plot
      plot(roc_obj,
        main = "Scorecard ROC Curve",
        print.auc = TRUE, print.thres = "best"
      )
    } else {
      message("Skipping validation - model not available.")
    }
    #> Scorecard Performance:
    #>   AUC:   0.6504
    #>   Gini:  0.3007
    #>   KS:    29.93 %

# Data Preprocessing

Proper preprocessing improves binning quality.

## Handling Missing Values and Outliers

    # Simulate feature with issues
    set.seed(2024)
    problematic <- c(
      rnorm(800, 5000, 2000), # Normal values
      rep(NA, 100), # Missing
      runif(100, -10000, 50000) # Outliers
    )

    target_sim <- rbinom(1000, 1, 0.3)

    # Preprocess with IQR method
    preproc_result <- ob_preprocess(
      feature = problematic,
      target = target_sim,
      outlier_method = "iqr",
      outlier_process = TRUE,
      preprocess = "both"
    )

    # View report
    print(preproc_result$report)
    #>   variable_type missing_count outlier_count
    #> 1       numeric           100            73
    #>                                                                                                       original_stats
    #> 1 { min: -8995.151324, Q1: 3697.784305, median: 5113.928039, mean: 6625.179803, Q3: 6705.149551, max: 49477.654407 }
    #>                                                                                                   preprocessed_stats
    #> 1 { min: -2161.216958, Q1: 3042.732437, median: 4792.608602, mean: 4703.116058, Q3: 6517.601547, max: 11703.913644 }

    # Compare distributions
    cat("\n\nBefore preprocessing:\n")
    #>
    #>
    #> Before preprocessing:
    cat("  Range:", range(problematic, na.rm = TRUE), "\n")
    #>   Range: -2161.217 11703.91
    cat("  Missing:", sum(is.na(problematic)), "\n")
    #>   Missing: 0
    cat("  Mean:", round(mean(problematic, na.rm = TRUE), 2), "\n")
    #>   Mean: 4703.12

    cat("\nAfter preprocessing:\n")
    #>
    #> After preprocessing:
    cleaned <- preproc_result$preprocess$feature_preprocessed
    cat("  Range:", range(cleaned), "\n")
    #>   Range: -2161.217 11703.91
    cat("  Missing:", sum(is.na(cleaned)), "\n")
    #>   Missing: 0
    cat("  Mean:", round(mean(cleaned), 2), "\n")
    #>   Mean: 4703.12

# Production Deployment

## Model Serialization

    # Add metadata to model
    sc_binning$metadata <- list(
      creation_date = Sys.time(),
      creator = Sys.info()["user"],
      dataset_size = nrow(train_sc),
      default_rate = mean(train_sc$default == "bad"),
      r_version = R.version.string,
      package_version = packageVersion("OptimalBinningWoE")
    )

    # Save model
    saveRDS(sc_binning, "credit_scorecard_v1_20250101.rds")

    # Load model
    loaded_model <- readRDS("credit_scorecard_v1_20250101.rds")

## Production Scoring Function

    score_applications <- function(new_data, model_file) {
      # Load binning model
      binning_model <- readRDS(model_file)

      # Validate required features
      required_vars <- binning_model$summary$feature[
        !binning_model$summary$error
      ]

      missing_vars <- setdiff(required_vars, names(new_data))
      if (length(missing_vars) > 0) {
        stop("Missing features: ", paste(missing_vars, collapse = ", "))
      }

      # Apply WoE transformation
      scored <- obwoe_apply(new_data, binning_model, keep_original = TRUE)

      # Add timestamp
      scored$scoring_date <- Sys.Date()

      return(scored)
    }

    # Usage example
    # new_apps <- read.csv("new_applications.csv")
    # scored_apps <- score_applications(new_apps, "credit_scorecard_v1_20250101.rds")

# Best Practices Summary

## Workflow Recommendations

1. Start Simple: Use `algorithm = "jedi"` as default
2. Check IV: Select features with IV ≥ 0.02
3. Validate Monotonicity: Use MOB/MBLP for regulatory models
4. Cross-Validate: Tune binning parameters with CV
5. Monitor Stability: Track WoE distributions over time
6. Document Thoroughly: Save metadata with models

## Common Pitfalls to Avoid

    # ❌ Don't bin on full dataset before splitting
    # This causes data leakage!
    bad_approach <- obwoe(full_data, target = "y")
    train_woe <- obwoe_apply(train_data, bad_approach)

    # ✅ Correct: Bin only on training data
    good_approach <- obwoe(train_data, target = "y")
    test_woe <- obwoe_apply(test_data, good_approach)

    # ❌ Don't ignore IV thresholds
    # IV > 0.50 likely indicates target leakage
    suspicious_features <- result$summary$feature[
      result$summary$total_iv > 0.50
    ]

    # ❌ Don't over-bin
    # Too many bins (>10) reduces interpretability
    # and may cause overfitting

# References

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

Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit Scoring and Its Applications. SIAM.

Navas-Palencia, G. (2020). Optimal Binning: Mathematical Programming Formulation. Expert Systems with Applications, 158, 113508.

Anderson, R. (2007). The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management. Oxford University Press.

# Session Information

    sessionInfo()
    #> R version 4.5.2 (2025-10-31 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows 11 x64 (build 26200)
    #>
    #> Matrix products: default
    #>   LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] LC_COLLATE=C                       LC_CTYPE=Portuguese_Brazil.utf8
    #> [3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
    #> [5] LC_TIME=Portuguese_Brazil.utf8
    #>
    #> time zone: America/Sao_Paulo
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #>  [1] pROC_1.19.0.1           yardstick_1.3.2         workflowsets_1.1.1
    #>  [4] workflows_1.3.0         tune_2.0.1              tidyr_1.3.2
    #>  [7] tailor_0.1.0            rsample_1.3.1           recipes_1.3.1
    #> [10] purrr_1.2.1             parsnip_1.4.1           modeldata_1.5.1
    #> [13] infer_1.1.0             ggplot2_4.0.1           dplyr_1.1.4
    #> [16] dials_1.4.2             scales_1.4.0            broom_1.0.11
    #> [19] tidymodels_1.4.1        scorecard_0.4.5         OptimalBinningWoE_1.0.8
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] tidyselect_1.2.1    timeDate_4051.111   farver_2.1.2
    #>  [4] S7_0.2.1            fastmap_1.2.0       digest_0.6.39
    #>  [7] rpart_4.1.24        timechange_0.3.0    lifecycle_1.0.5
    #> [10] survival_3.8-3      magrittr_2.0.4      compiler_4.5.2
    #> [13] rlang_1.1.7         sass_0.4.10         tools_4.5.2
    #> [16] utf8_1.2.6          yaml_2.3.12         data.table_1.18.0
    #> [19] knitr_1.51          labeling_0.4.3      xml2_1.5.1
    #> [22] DiceDesign_1.10     RColorBrewer_1.1-3  withr_3.0.2
    #> [25] nnet_7.3-20         grid_4.5.2          sparsevctrs_0.3.6
    #> [28] future_1.69.0       globals_0.18.0      iterators_1.0.14
    #> [31] MASS_7.3-65         cli_3.6.5           rmarkdown_2.30
    #> [34] generics_0.1.4      otel_0.2.0          rstudioapi_0.17.1
    #> [37] future.apply_1.20.1 cachem_1.1.0        splines_4.5.2
    #> [40] parallel_4.5.2      vctrs_0.6.5         hardhat_1.4.2
    #> [43] Matrix_1.7-4        jsonlite_2.0.0      listenv_0.10.0
    #> [46] foreach_1.5.2       gower_1.0.2         jquerylib_0.1.4
    #> [49] glue_1.8.0          parallelly_1.46.1   codetools_0.2-20
    #> [52] lubridate_1.9.4     stringi_1.8.7       gtable_0.3.6
    #> [55] GPfit_1.0-9         tibble_3.3.1        furrr_0.3.1
    #> [58] xefun_0.1.5         pillar_1.11.1       htmltools_0.5.9
    #> [61] ipred_0.9-15        lava_1.8.2          R6_2.6.1
    #> [64] lhs_1.2.0           doParallel_1.0.17   evaluate_1.0.5
    #> [67] lattice_0.22-7      backports_1.5.0     openxlsx_4.2.8.1
    #> [70] bslib_0.9.0         class_7.3-23        Rcpp_1.1.1
    #> [73] zip_2.3.3           gridExtra_2.3       prodlim_2025.04.28
    #> [76] xfun_0.55           pkgconfig_2.0.3