Optimal binning: mathematical programming formulation
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Abstract (arXiv)
The optimal binning is the optimal discretization of a variable into bins given a discrete or continuous numeric target. We present a rigorous and extensible mathematical programming formulation for solving the optimal binning problem for a binary, continuous and multi-class target type, incorporating constraints not previously addressed. For all three target types, we introduce a convex mixed-integer programming formulation. Several algorithmic enhancements, such as automatic determination of the most suitable monotonic trend via a Machine-Learning-based classifier and implementation aspects are thoughtfully discussed. The new mathematical programming formulations are carefully implemented in the open-source python library OptBinning.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jan 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 3; 2025: 0; 2026: 1.
9 papers.
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- 2023PiML Toolbox for Interpretable Machine Learning Model Development and Diagnosticscited by 4
- 2023From Principle to Practice: Vertical Data Minimization for Machine Learningcited by 3
- 2024Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classificationcited by 2
- 2023Statistical Hypothesis Testing for Information Value (IV)cited by 0
- 2024A machine learning workflow to address credit default predictioncited by 0
- 2026Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experimentscited by 0
- 2026Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experiments20 Sep 2026
- 2024Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification24 Oct 2024
- 2024Powering In-Database Dynamic Model Slicing for Structured Data Analytics1 May 2024
- 2024A machine learning workflow to address credit default prediction6 Mar 2024
- 2023From Principle to Practice: Vertical Data Minimization for Machine Learning17 Nov 2023
- 2023Statistical Hypothesis Testing for Information Value (IV)22 Sep 2023
- 2023PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics7 May 2023
- 2021Learning Optimal Predictive Checklists2 Dec 2021
- 2021Optimal Counterfactual Explanations for Scorecard modelling17 Apr 2021