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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
On Explaining Random Forests with SAT
Authors: Yacine Izza, Joao Marques-Silva
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,873 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,497 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results, obtained on a wide range of publicly available datasets, demonstrate that the proposed SAT-based approach scales to RFs of sizes common in practical applications. |
| Researcher Affiliation | Academia | 1University of Toulouse, France 2IRIT, CNRS, Toulouse, France |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement or link indicating that the authors are releasing the source code for the methodology described in this paper. |
| Open Datasets | Yes | The assessment is performed on a selection of 32 publicly available datasets, which originate from UCI Machine Learning Repository [Dua and Graff, 2017] and Penn Machine Learning Benchmarks [Olson et al., 2017]. |
| Dataset Splits | Yes | When training RF classi๏ฌers for the selected datasets, we used 80% of the dataset instances (20% used for test data). |
| Hardware Specification | Yes | The experiments are conducted on a Mac Book Pro with a Dual-Core Intel Core i5 2.3GHz CPU with 8GByte RAM running mac OS Catalina. |
| Software Dependencies | No | The paper mentions 'scikit-learn ML tool' and 'Py SAT [Ignatiev et al., 2018]' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The number of trees in each RF is set to 100 while tree depth varies between 3 and 8. |