2020/01/22 by Guillermo Navas-Palencia, Navas-Palencia, Guillermo
Computer Science · #Algorithms and Data Compression #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2001.08025
openalex publication_date 2020/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.