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OptimalBinningWoE

Overview

OptimalBinningWoE is a high-performance R package for optimal binning and Weight of Evidence (WoE) transformation, designed for credit scoring, risk assessment, and predictive modeling applications.

Why OptimalBinningWoE?

Installation

# Install from CRAN (when available)
install.packages("OptimalBinningWoE")

# Or install the development version from GitHub
# install.packages("pak")
pak::pak("evandeilton/OptimalBinningWoE")

Quick Start

Basic Usage

library(OptimalBinningWoE)

# Create sample data
set.seed(123)
df <- data.frame(
  age = rnorm(1000, 45, 15),
  income = exp(rnorm(1000, 10, 0.5)),
  education = sample(c("HS", "BA", "MA", "PhD"), 1000, replace = TRUE),
  target = rbinom(1000, 1, 0.15)
)

# Automatic optimal binning with WoE calculation
result <- obwoe(
  data = df,
  target = "target",
  algorithm = "jedi", # Joint Entropy-Driven Information
  min_bins = 3,
  max_bins = 6
)

# View summary
print(result)

# Examine binning details
result$results$age

Integration with tidymodels

library(tidymodels)
library(OptimalBinningWoE)

# Create a preprocessing recipe with WoE transformation
rec <- recipe(default ~ ., data = credit_data) %>%
  step_obwoe(
    all_predictors(),
    outcome = "default",
    algorithm = "mob", # Monotonic Optimal Binning
    min_bins = 3,
    max_bins = tune(), # Tune the number of bins
    output = "woe"
  )

# Works seamlessly in ML workflows
workflow() %>%
  add_recipe(rec) %>%
  add_model(logistic_reg()) %>%
  fit(data = training_data)

Core Concepts

Weight of Evidence (WoE)

WoE quantifies the predictive power of each bin by measuring the log-odds ratio:

\[\text{WoE}_i = \ln\left(\frac{\text{Distribution of Goods}_i}{\text{Distribution of Bads}_i}\right)\]

Interpretation:

• WoE > 0: Lower risk than average (more “goods” than expected)
• WoE < 0: Higher risk than average (more “bads” than expected)
• WoE ≈ 0: Similar to population average

Information Value (IV)

IV measures the overall predictive power of a feature:

\[\text{IV} = \sum_{i=1}^{n} (\text{Dist. Goods}_i - \text{Dist. Bads}_i) \times \text{WoE}_i\]

Algorithm Reference

OptimalBinningWoE provides 36 algorithms optimized for different scenarios:

Universal Algorithms (Numerical & Categorical)

Numerical-Only Algorithms (20)

Categorical-Only Algorithms (16)

Algorithm Selection Guide

Key Functions

Example Workflow

library(OptimalBinningWoE)

# 1. Fit binning model on training data
model <- obwoe(
  data = train_data,
  target = "default",
  algorithm = "mob",
  min_bins = 3,
  max_bins = 5
)

# 2. View feature importance by IV
print(model$summary[order(-model$summary$total_iv), ])

# 3. Apply transformation
train_woe <- obwoe_apply(train_data, model)
test_woe <- obwoe_apply(test_data, model)

# 4. Compute performance metrics
gains <- obwoe_gains(model, feature = "income")
print(gains)
plot(gains, type = "ks")

Performance

OptimalBinningWoE is optimized for speed through:

• RcppEigen: Vectorized linear algebra operations
• Efficient algorithms: O(n log n) or better complexity
• Memory-conscious design: Streaming algorithms for large data

Typical performance on a standard laptop:

Documentation

• 📖 Package Vignette: Comprehensive guide with examples
• 📚 Function Reference: Complete API documentation
• 🐛 Issue Tracker: Report bugs or request features

Contributing

Contributions are welcome! Please see our Contributing Guidelines and Code of Conduct.

Citation

If you use OptimalBinningWoE in your research, please cite:

@software{optimalbinningwoe,
  author = {José Evandeilton Lopes},
  title = {OptimalBinningWoE: Optimal Binning and Weight of Evidence Framework for Modeling},
  year = {2026},
  url = {https://github.com/evandeilton/OptimalBinningWoE}
}

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. arXiv:2001.08025.

License

MIT License © 2026 José Evandeilton Lopes

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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