Package: OptimalBinningWoE 1.10.0

Introduction | Package Overview | Theoretical Foundation | Weight of Evidence (WoE) | Information Value (IV) | Installation | Dataset: German Credit Data | Data Preparation | Quick Start: Single Feature Binning | Visualize Binning Results | Key Insights from Single Feature | Multiple Features: Automated Binning | Feature Selection by IV | Gains Table Analysis | Algorithm Comparison | Algorithm Selection Guide | Algorithm Selection by Use Case | Complete Algorithm List (36 Algorithms) | Production Pipeline with tidymodels | Define Preprocessing Recipe | Model Specification and Workflow | Hyperparameter Tuning | Final Model Fitting | Model Evaluation | Inspect Learned Binning Rules | Traditional Scorecard Development | Train-Test Split | Fit Optimal Binning | Apply WoE Transformation | Build Logistic Regression | Scorecard Validation | Data Preprocessing | Handling Missing Values and Outliers | Production Deployment | Model Serialization | Production Scoring Function | Best Practices Summary | Workflow Recommendations | Common Pitfalls to Avoid | References | Session Information