slim-python
is a package to learn customized scoring systems for decision-making problems.
These are simple decision aids that let users make yes-no predictions by adding and subtracting a few small numbers.
SLIM is designed to learn the most accurate scoring system for a given dataset and set of constraints. These models are produced by solving a hard optimization problem that directly optimizes for accuracy, sparsity, and customized constraints (e.g., hard limits on model size, TPR, FPR).
slim-python
was developed using Python 2.7.11 and CPLEX 12.6.2.
CPLEX is cross-platform commercial optimization tool with a Pytho API. It is freely available to students and faculty members at accredited institutions as part of the IBM Academic Initiative. To get CPLEX:
Please check the CPLEX user manual or the CPLEX forums if you have problems installing CPLEX.
If you use SLIM for academic research, please cite our paper!
@article{
ustun2015slim,
year = {2015},
issn = {0885-6125},
journal = {Machine Learning},
doi = {10.1007/s10994-015-5528-6},
title = {Supersparse linear integer models for optimized medical scoring systems},
url = {http://dx.doi.org/10.1007/s10994-015-5528-6},
publisher = { Springer US},
author = {Ustun, Berk and Rudin, Cynthia},
pages = {1-43},
language = {English}
}