Source code and data for the paper "Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming"

doi:10.4121/cb4f4468-05d1-4309-bc4b-d74748e3cfba.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/cb4f4468-05d1-4309-bc4b-d74748e3cfba
Datacite citation style:
van der Linden, Jacobus G. M.; de Weerdt, M.M. (Mathijs); Demirovic, Emir (2023): Source code and data for the paper "Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming". Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/cb4f4468-05d1-4309-bc4b-d74748e3cfba.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software

STreeD is a framework for optimal binary decision trees with separable optimization tasks. A separable optimization task is a task that can be optimized separately for the left and right subtree. The current STreeD Framework implements a broad set of such optimization tasks, from group fairness constraints to prescriptive policy generation. For an explanation of each application, see below. For details on what tasks are separable and how the algorithm works, see our paper.

history
  • 2023-11-01 first online, published, posted
publisher
4TU.ResearchData
format
text/x-python
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology

DATA

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/2aab49f2-a61b-456d-88d0-679b3e9103ac.git "pystreed"

Or download the latest commit as a ZIP.

files (1)