Optimal Binning and Weight of Evidence Framework for Modeling
Apply the Optimal Binning Transformation
Control Parameters for Optimal Binning Algorithms
Categorical-Only Algorithms
Internal Algorithm Dispatcher
Get Algorithm Registry
Numerical-Only Algorithms
Universal Algorithms
Valid Binning Algorithms
Fit Logistic Regression Model
Optimal Binning for Numerical Variables using Dynamic Programming
Hybrid Optimal Binning using Equal-Width Initialization and IV Optimiz...
Optimal Binning using MDLP with Monotonicity Constraints
Optimal Binning using Fisher's Exact Test
Optimal Binning using Isotonic Regression (PAVA)
Optimal Binning for Multiclass Targets using JEDI M-WOE
Optimal Binning using Joint Entropy-Driven Interval Discretization (JE...
Optimal Binning using K-means Inspired Initialization (KMB)
Optimal Binning for Numerical Variables using Local Density Binning
Optimal Binning using Local Polynomial Density Binning (LPDB)
Optimal Binning for Numerical Features Using Monotonic Binning via Lin...
Optimal Binning for Numerical Features using Minimum Description Lengt...
Optimal Binning for Numerical Features using Monotonic Optimal Binning
Optimal Binning for Numerical Features using Monotonic Risk Binning wi...
Optimal Binning for Numerical Variables using Optimal Supervised Learn...
Optimal Binning for Numerical Variables using Sketch-based Algorithm
Optimal Binning for Numerical Variables using Unsupervised Binning wit...
Optimal Binning for Numerical Variables using Entropy-Based Partitioni...
Data Preprocessor for Optimal Binning
Compute Multiple Robust Correlations Between Numeric Variables
Binning Algorithm Parameter
List Available Algorithms
Apply Weight of Evidence Transformations to New Data
Bin Cutoff Parameter
Gains Table Statistics for Credit Risk Scorecard Evaluation
Maximum Bins Parameter
Minimum Bins Parameter
Unified Optimal Binning and Weight of Evidence Transformation
Plot Gains Table
Plot Method for obwoe Objects
Prepare the Optimal Binning Step
Print Method for obwoe Objects
Print Method for step_obwoe
Required Packages for step_obwoe
Internal Constructor for step_obwoe
Optimal Binning and WoE Transformation Step
Optimal Binning for Categorical Variables using Simulated Annealing
Optimal Binning for Categorical Variables using SBLP
Optimal Binning for Categorical Variables using Sketch-based Algorithm
Optimal Binning for Categorical Variables using Sliding Window Binning...
Optimal Binning for Categorical Variables using a User-Defined Techniq...
Apply Optimal Weight of Evidence (WoE) to a Categorical Feature
Apply Optimal Weight of Evidence (WoE) to a Numerical Feature
Optimal Binning for Categorical Variables using Enhanced ChiMerge Algo...
Optimal Binning for Categorical Variables using Divergence Measures
Optimal Binning for Categorical Variables using Dynamic Programming
Optimal Binning for Categorical Variables using Fisher's Exact Test
Optimal Binning for Categorical Variables using Greedy Merge Algorithm
Optimal Binning for Categorical Variables using Information Value Dyna...
Optimal Binning for Categorical Variables with Multinomial Target usin...
Optimal Binning for Categorical Variables using JEDI Algorithm
Optimal Binning for Categorical Variables using Monotonic Binning Algo...
Optimal Binning for Categorical Variables using Heuristic Algorithm
Optimal Binning for Categorical Variables using Monotonic Optimal Binn...
Check Distinct Length
Binning Categorical Variables using Custom Cutpoints
Binning Numerical Variables using Custom Cutpoints
Compute Gains Table for a Binned Feature Vector
Compute Comprehensive Gains Table from Binning Results
Optimal Binning for Numerical Variables using Branch and Bound Algorit...
Optimal Binning for Numerical Variables using Enhanced ChiMerge Algori...
Optimal Binning using Metric Divergence Measures (Zeng, 2013)
Summary Method for obwoe Objects
Tidy Method for step_obwoe
Tunable Parameters for step_obwoe
High-performance implementation of 36 optimal binning algorithms (16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Includes advanced methods such as Mixed Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, and Monotonic Regression. Features automatic method selection based on Information Value ('IV') maximization, strict monotonicity enforcement, and efficient handling of large datasets via 'Rcpp'. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) <doi:10.1002/9781119201731> and Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025>.
Useful links