Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
Authors: Jacobus van der Linden, Mathijs de Weerdt, Emir Demirović
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on five application domains show the general applicability of this framework, while outperforming the scalability of general-purpose solvers by a large margin. |
| Researcher Affiliation | Academia | Jacobus G. M. van der Linden Mathijs M. de Weerdt Emir Demirovi c Delft University of Technology, Department of Computer Science EMAIL |
| Pseudocode | Yes | Pseudo-code for STree D and additional algorithmic techniques for speeding up computation, such as a special depth-two solver, caching, and upper and lower bounds, as well as techniques for sparse trees and hypertuning, can be found in Appendix B. |
| Open Source Code | Yes | STree D is implemented in C++ and is available as a Python package.1 |
| Open Datasets | Yes | Table 1 lists the datasets used in this experiment [24, 41, 95]. |
| Dataset Splits | No | Every dataset is split randomly 100 times in a train and test set, with 80% and 20% of the instances respectively. |
| Hardware Specification | Yes | All experiments are run on a 2.6 GHz Intel i7 CPU with 8GB RAM using only one thread. |
| Software Dependencies | Yes | MIP models are solved using Gurobi 9.0 with default parameters [33]. |
| Experiment Setup | Yes | We tune STree D with a depth limit of d = 4. In Appendix C we show extended results and also compare with several heuristics from the literature. To our knowledge, no MIP methods for cost-sensitive classification including group discounts (i.e., when certain features are tested together, the feature cost decreases) exist, and therefore are not considered here. |