BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning
This provides a practical tool for researchers and practitioners in machine learning to efficiently handle interpretable models like sparse regression and decision trees, though it is incremental as it builds on existing MIO frameworks.
The authors tackled the challenge of scaling mixed-integer optimization (MIO) problems with indicator variables for high-dimensional machine learning tasks, resulting in a library that solves problems faster than exact methods and with higher accuracy than common heuristics.
We present BackboneLearn: an open-source software package and framework for scaling mixed-integer optimization (MIO) problems with indicator variables to high-dimensional problems. This optimization paradigm can naturally be used to formulate fundamental problems in interpretable supervised learning (e.g., sparse regression and decision trees), in unsupervised learning (e.g., clustering), and beyond; BackboneLearn solves the aforementioned problems faster than exact methods and with higher accuracy than commonly used heuristics. The package is built in Python and is user-friendly and easily extensible: users can directly implement a backbone algorithm for their MIO problem at hand. The source code of BackboneLearn is available on GitHub (link: https://github.com/chziakas/backbone_learn).