On Explaining Random Forests with SAT
Authors: Yacine Izza, Joao Marques-Silva
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 classifiers 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. |