ob_categorical_mba: Optimal Binning for Categorical Variables using Monotonic Binning Algorithm

Description

Performs supervised discretization of categorical variables using the Monotonic Binning Algorithm (MBA), which enforces strict Weight of Evidence monotonicity while optimizing Information Value through intelligent bin merging strategies. This implementation includes Bayesian smoothing for numerical stability and adaptive thresholding for robust monotonicity enforcement.

Usage

ob_categorical_mba(
  feature,
  target,
  min_bins = 3,
  max_bins = 5,
  bin_cutoff = 0.05,
  max_n_prebins = 20,
  bin_separator = "%;%",
  convergence_threshold = 1e-06,
  max_iterations = 1000
)

Value

A list containing the binning results with the following components:

id: Integer vector of bin identifiers (1-indexed)
bin: Character vector of bin labels (merged category names)
woe: Numeric vector of Weight of Evidence values per bin
iv: Numeric vector of Information Value contribution per bin
count: Integer vector of total observations per bin
count_pos: Integer vector of positive cases (target=1) per bin
count_neg: Integer vector of negative cases (target=0) per bin
total_iv: Numeric total Information Value of the binning solution
converged: Logical indicating algorithm convergence
iterations: Integer count of optimization iterations performed

Arguments

feature: A character vector or factor representing the categorical predictor variable to be binned. Missing values are automatically converted to the category "NA".
target: An integer vector of binary outcomes (0/1) corresponding to each observation in feature. Missing values are not permitted.
min_bins: Integer. Minimum number of bins to produce. Must be >= 2. The algorithm will not merge below this threshold. Defaults to 3.
max_bins: Integer. Maximum number of bins to produce. Must be >= min_bins. The algorithm reduces bins until this constraint is met. Defaults to 5.
bin_cutoff: Numeric. Minimum proportion of total observations required for a category to remain separate. Categories below this threshold are pre-merged with similar categories. Must be in (0, 1). Defaults to 0.05.
max_n_prebins: Integer. Maximum number of initial bins before the main optimization phase. Controls computational complexity. Must be >= max_bins. Defaults to 20.
bin_separator: Character string used to concatenate category names when multiple categories are merged into a single bin. Defaults to "%;%".
convergence_threshold: Numeric. Convergence tolerance based on Information Value change between iterations. Algorithm stops when $|\Delta IV| <$ convergence_threshold. Must be > 0. Defaults to 1e-6.
max_iterations: Integer. Maximum number of optimization iterations. Prevents infinite loops. Must be > 0. Defaults to 1000.

Details

The Monotonic Binning Algorithm (MBA) implements a sophisticated approach to categorical binning that guarantees strict Weight of Evidence monotonicity through intelligent violation detection and repair mechanisms.

Algorithm Workflow:

Input validation and preprocessing
Initial bin creation (one category per bin)
Pre-binning limitation to max_n_prebins
Rare category merging (frequencies < bin_cutoff)
Bayesian-smoothed WoE calculation
Strict monotonicity enforcement with adaptive thresholds
IV-optimized bin merging to meet max_bins constraint
Final consistency verification

Monotonicity Enforcement:

MBA enforces strict monotonicity through an iterative repair process:

Sort bins by current WoE values
Calculate adaptive threshold: $\tau = \min(\epsilon, 0.01\bar{\Delta})$
Identify violations: $sign(WoE_i - WoE_{i-1}) \neq sign(WoE_{i+1} - WoE_i)$
Rank violations by severity: $s_i = |WoE_i - WoE_{i-1}| + |WoE_{i+1} - WoE_i|$
Repair most severe violations by merging adjacent bins
Repeat until no violations remain or min_bins reached

Bayesian Smoothing:

To ensure numerical stability and prevent overfitting, MBA applies Bayesian smoothing to WoE and IV calculations:

$$p'_i = \frac{n_{i,pos} + \alpha_p}{N_{pos} + \alpha_{total}}$$ $$n'_i = \frac{n_{i,neg} + \alpha_n}{N_{neg} + \alpha_{total}}$$

where priors are proportional to overall prevalence:

$$\alpha_p = \alpha_{total} \times \frac{N_{pos}}{N_{pos} + N_{neg}}$$ $$\alpha_n = \alpha_{total} - \alpha_p$$

with $\alpha_{total} = 1.0$ as the prior strength parameter.

Intelligent Bin Merging:

When reducing bins to meet the max_bins constraint, MBA employs an IV-loss minimization strategy:

$$\Delta IV_{i,j} = IV_i + IV_j - IV_{merged}(i,j)$$

The pair with minimum $\Delta IV$ is merged to preserve maximum predictive information.

Computational Complexity:

Time: $O(k^2 \cdot m)$ where $k$ = bins, $m$ = iterations
Space: $O(k^2)$ for IV loss cache
Cache hit rate typically > 75% for $k > 10$

Key Features:

Guaranteed monotonicity: Strict enforcement with adaptive thresholds
Bayesian regularization: Robust to sparse bins and class imbalance
Intelligent merging: Preserves maximum information during reduction
Adaptive thresholds: Context-aware violation detection
Consistency verification: Final integrity checks

References

Mironchyk, P., & Tchistiakov, V. (2017). Monotone optimal binning algorithm for credit risk modeling. SSRN Electronic Journal. tools:::Rd_expr_doi("10.2139/ssrn.2978774")

Siddiqi, N. (2017). Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards (2nd ed.). Wiley.

Good, I. J. (1965). The Estimation of Probabilities: An Essay on Modern Bayesian Methods. MIT Press.

Zeng, G. (2014). A necessary condition for a good binning algorithm in credit scoring. Applied Mathematical Sciences, 8(65), 3229-3242.

Examples

Run this code

# \donttest{
# Example 1: Basic monotonic binning with guaranteed WoE ordering
set.seed(42)
n_obs <- 1500

# Simulate risk ratings with natural monotonic relationship
ratings <- c("AAA", "AA", "A", "BBB", "BB", "B", "CCC")
default_probs <- c(0.01, 0.02, 0.05, 0.10, 0.20, 0.35, 0.50)

cat_feature <- sample(ratings, n_obs,
  replace = TRUE,
  prob = c(0.05, 0.10, 0.20, 0.25, 0.20, 0.15, 0.05)
)
bin_target <- sapply(cat_feature, function(x) {
  rbinom(1, 1, default_probs[which(ratings == x)])
})

# Apply MBA algorithm
result_mba <- ob_categorical_mba(
  cat_feature,
  bin_target,
  min_bins = 3,
  max_bins = 5
)

# Display results with guaranteed monotonic WoE
print(data.frame(
  Bin = result_mba$bin,
  WoE = round(result_mba$woe, 3),
  IV = round(result_mba$iv, 4),
  Count = result_mba$count,
  EventRate = round(result_mba$count_pos / result_mba$count, 3)
))

cat("\nMonotonicity check (WoE differences):\n")
woe_diffs <- diff(result_mba$woe)
cat("  Differences:", paste(round(woe_diffs, 4), collapse = ", "), "\n")
cat("  All positive (increasing):", all(woe_diffs >= -1e-10), "\n")
cat("  Total IV:", round(result_mba$total_iv, 4), "\n")
cat("  Converged:", result_mba$converged, "\n")

# Example 2: Comparison with non-monotonic methods
set.seed(123)
n_obs_comp <- 2000

sectors <- c("Tech", "Health", "Finance", "Manufacturing", "Retail")
cat_feature_comp <- sample(sectors, n_obs_comp, replace = TRUE)
bin_target_comp <- rbinom(n_obs_comp, 1, 0.15)

# MBA (strictly monotonic)
result_mba_comp <- ob_categorical_mba(
  cat_feature_comp, bin_target_comp,
  min_bins = 3, max_bins = 4
)

# Standard binning (may not be monotonic)
result_std_comp <- ob_categorical_cm(
  cat_feature_comp, bin_target_comp,
  min_bins = 3, max_bins = 4
)

cat("\nMonotonicity comparison:\n")
cat(
  "  MBA WoE differences:",
  paste(round(diff(result_mba_comp$woe), 4), collapse = ", "), "\n"
)
cat("  MBA monotonic:", all(diff(result_mba_comp$woe) >= -1e-10), "\n")
cat(
  "  Std WoE differences:",
  paste(round(diff(result_std_comp$woe), 4), collapse = ", "), "\n"
)
cat("  Std monotonic:", all(diff(result_std_comp$woe) >= -1e-10), "\n")

# Example 3: Bayesian smoothing with sparse data
set.seed(789)
n_obs_sparse <- 400

# Small sample with rare categories
categories <- c("A", "B", "C", "D", "E", "F")
cat_probs <- c(0.30, 0.25, 0.20, 0.15, 0.07, 0.03)

cat_feature_sparse <- sample(categories, n_obs_sparse,
  replace = TRUE,
  prob = cat_probs
)
bin_target_sparse <- rbinom(n_obs_sparse, 1, 0.08) # 8% event rate

result_mba_sparse <- ob_categorical_mba(
  cat_feature_sparse,
  bin_target_sparse,
  min_bins = 2,
  max_bins = 4,
  bin_cutoff = 0.02
)

cat("\nBayesian smoothing (sparse data):\n")
cat("  Sample size:", n_obs_sparse, "\n")
cat("  Events:", sum(bin_target_sparse), "\n")
cat("  Final bins:", length(result_mba_sparse$bin), "\n\n")

# Show how smoothing prevents extreme WoE values
for (i in seq_along(result_mba_sparse$bin)) {
  cat(sprintf(
    "  Bin %d: events=%d/%d, WoE=%.3f (smoothed)\n",
    i,
    result_mba_sparse$count_pos[i],
    result_mba_sparse$count[i],
    result_mba_sparse$woe[i]
  ))
}

# Example 4: High cardinality with pre-binning
set.seed(456)
n_obs_hc <- 3000

# Simulate ZIP codes (high cardinality)
zips <- paste0("ZIP_", sprintf("%04d", 1:50))

cat_feature_hc <- sample(zips, n_obs_hc, replace = TRUE)
bin_target_hc <- rbinom(n_obs_hc, 1, 0.12)

result_mba_hc <- ob_categorical_mba(
  cat_feature_hc,
  bin_target_hc,
  min_bins = 4,
  max_bins = 6,
  max_n_prebins = 20,
  bin_cutoff = 0.01
)

cat("\nHigh cardinality performance:\n")
cat("  Original categories:", length(unique(cat_feature_hc)), "\n")
cat("  Final bins:", length(result_mba_hc$bin), "\n")
cat(
  "  Largest merged bin contains:",
  max(sapply(strsplit(result_mba_hc$bin, "%;%"), length)), "categories\n"
)

# Verify monotonicity in high-cardinality case
woe_monotonic <- all(diff(result_mba_hc$woe) >= -1e-10)
cat("  WoE monotonic:", woe_monotonic, "\n")

# Example 5: Convergence behavior
set.seed(321)
n_obs_conv <- 1000

business_sizes <- c("Micro", "Small", "Medium", "Large", "Enterprise")
cat_feature_conv <- sample(business_sizes, n_obs_conv, replace = TRUE)
bin_target_conv <- rbinom(n_obs_conv, 1, 0.18)

# Test different convergence thresholds
thresholds <- c(1e-3, 1e-6, 1e-9)

for (thresh in thresholds) {
  result_conv <- ob_categorical_mba(
    cat_feature_conv,
    bin_target_conv,
    min_bins = 2,
    max_bins = 4,
    convergence_threshold = thresh,
    max_iterations = 50
  )

  cat(sprintf("\nThreshold %.0e:\n", thresh))
  cat("  Final bins:", length(result_conv$bin), "\n")
  cat("  Total IV:", round(result_conv$total_iv, 4), "\n")
  cat("  Converged:", result_conv$converged, "\n")
  cat("  Iterations:", result_conv$iterations, "\n")

  # Check monotonicity preservation
  monotonic <- all(diff(result_conv$woe) >= -1e-10)
  cat("  Monotonic:", monotonic, "\n")
}

# Example 6: Missing value handling
set.seed(555)
cat_feature_na <- cat_feature
na_indices <- sample(n_obs, 75) # 5% missing
cat_feature_na[na_indices] <- NA

result_mba_na <- ob_categorical_mba(
  cat_feature_na,
  bin_target,
  min_bins = 3,
  max_bins = 5
)

# Locate NA bin
na_bin_idx <- grep("NA", result_mba_na$bin)
if (length(na_bin_idx) > 0) {
  cat("\nMissing value treatment:\n")
  cat("  NA bin:", result_mba_na$bin[na_bin_idx], "\n")
  cat("  NA count:", result_mba_na$count[na_bin_idx], "\n")
  cat(
    "  NA event rate:",
    round(result_mba_na$count_pos[na_bin_idx] /
      result_mba_na$count[na_bin_idx], 3), "\n"
  )
  cat("  NA WoE:", round(result_mba_na$woe[na_bin_idx], 3), "\n")
  cat(
    "  Monotonicity preserved:",
    all(diff(result_mba_na$woe) >= -1e-10), "\n"
  )
}
# }

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