  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).
