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Optimal Binning using Isotonic Regression (PAVA)

Source: R/obn_ir.R
ob_numerical_ir.Rd

Performs supervised discretization of continuous numerical variables using Isotonic Regression (specifically the Pool Adjacent Violators Algorithm - PAVA). This method ensures a strictly monotonic relationship between bin indices and the empirical event rate, making it ideal for applications requiring shape constraints like credit scoring.

Usage

ob_numerical_ir(
  feature,
  target,
  min_bins = 3,
  max_bins = 5,
  bin_cutoff = 0.05,
  max_n_prebins = 20,
  auto_monotonicity = TRUE,
  convergence_threshold = 1e-06,
  max_iterations = 1000
)

Arguments

feature

A numeric vector representing the continuous predictor variable. Missing values (NA) are excluded from the binning process.

target

An integer vector of binary outcomes (0/1) corresponding to each observation in feature. Must have the same length as feature.

min_bins

Integer. The minimum number of bins to produce. Must be \(\ge\) 2. Defaults to 3.

max_bins

Integer. The maximum number of bins to produce. Must be \(\ge\) min_bins. Defaults to 5.

bin_cutoff

Numeric. The minimum fraction of total observations required for a bin to be considered valid. Bins with frequency < bin_cutoff will be merged with neighbors. Value must be in (0, 1). Defaults to 0.05.

max_n_prebins

Integer. The number of initial quantiles to generate during the pre-binning phase. Defaults to 20.

auto_monotonicity

Logical. If TRUE, the algorithm automatically determines the optimal monotonicity direction (increasing or decreasing) based on the Pearson correlation between feature values and target. If FALSE, defaults to increasing monotonicity. Defaults to TRUE.

convergence_threshold

Numeric. Reserved for future use. Currently not actively used by the PAVA algorithm, which has guaranteed convergence. Defaults to 1e-6.

max_iterations

Integer. Safety limit for iterative merging operations during pre-processing steps (e.g., rare bin merging). Defaults to 1000.

Value

A list containing the binning results:

  • id: Integer vector of bin identifiers.

  • bin: Character vector of bin labels in interval notation.

  • woe: Numeric vector of Weight of Evidence for each 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.

  • count_neg: Integer vector of negative cases.

  • cutpoints: Numeric vector of upper boundaries (excluding Inf).

  • total_iv: The total Information Value of the binned variable.

  • monotone_increasing: Logical indicating if the final WoE trend is increasing.

  • converged: Logical indicating successful completion.

Details

This function implements a shape-constrained binning approach using Isotonic Regression. Unlike heuristic merging strategies (ChiMerge, DP), this method finds the optimal monotonic fit in a single pass.

Core Algorithm (PAVA): The Pool Adjacent Violators Algorithm (Best & Chakravarti, 1990) is used to transform the empirical event rates of initial bins into a sequence that is either monotonically increasing or decreasing. It works by scanning the sequence and merging ("pooling") any adjacent pairs that violate the desired trend until a perfect fit is achieved. This guarantees an optimal solution in \(O(n)\) time.

Process Flow:

  1. Pre-binning: Creates initial bins using quantiles.

  2. Stabilization: Merges bins below bin_cutoff.

  3. Trend Detection: If auto_monotonicity = TRUE, calculates the correlation between feature midpoints and bin event rates to determine if the relationship should be increasing or decreasing.

  4. Shape Enforcement: Applies PAVA to the sequence of bin event rates, producing a new set of rates that conform exactly to the monotonic constraint.

  5. Metric Calculation: Derives WoE and IV from the adjusted rates.

Advantages:

  • Global Optimality: PAVA finds the best fit under the monotonicity constraint.

  • No Hyperparameters: Unlike ChiMerge's p-value threshold, PAVA requires no significance level tuning for the core regression step.

  • Robustness: Less sensitive to arbitrary thresholds compared to greedy merging.

References

Barlow, R. E., Bartholomew, D. J., Bremner, J. M., & Brunk, H. D. (1972). Statistical inference under order restrictions. John Wiley & Sons.

Best, M. J., & Chakravarti, N. (1990). Active set algorithms for isotonic regression; A unifying framework. Mathematical Programming, 47(1-3), 425-439.

See also

ob_numerical_dp for greedy dynamic programming approaches.

Examples

# Example: Forcing a monotonic WoE trend
set.seed(123)
feature <- rnorm(500)
# Create a slightly noisy but generally increasing relationship
prob <- plogis(0.5 * feature + rnorm(500, 0, 0.3))
target <- rbinom(500, 1, prob)

result <- ob_numerical_ir(feature, target,
  min_bins = 4,
  max_bins = 6,
  auto_monotonicity = TRUE
)

print(result$bin)
#> [1] "(-Inf;-0.945409]"      "(-0.945409;-0.388780]" "(-0.388780;0.020451]" 
#> [4] "(0.020451;0.418982]"   "(0.418982;0.976973]"   "(0.976973;+Inf]"      
print(round(result$woe, 3))
#> [1] -0.339 -0.070 -0.096 -0.096  0.071  0.532
print(paste("Monotonic Increasing:", result$monotone_increasing))
#> [1] "Monotonic Increasing: TRUE"