On the Computation of Example-Based Abductive Explanations for Random Forests
Authors: Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in the case of random forest classifiers show that our CEGAR-based algorithm is quite efficient in practice. |
| Researcher Affiliation | Academia | 1Univ. Artois, CNRS, CRIL, F-62300 Lens, France 2Institut Universitaire de France {audemard, lagniez, marquis, szczepanski}@cril.fr |
| Pseudocode | No | The paper describes the algorithmic steps and components (e.g., 'Our approach relies on a two-phase procedure', 'Our algorithm is based on linear search') but does not include a formally labeled 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | Yes | Additional empirical results and the code used in our experiments are also furnished in this supplementary material. |
| Open Datasets | Yes | We have focused on 14 datasets issued from three well-known repositories, namely Open ML1 (openml.org), UCI2 (archive.ics.uci.edu/ml/), and UCR3 (timeseriesclassification.com). |
| Dataset Splits | Yes | A 10-fold cross validation process has been achieved. |
| Hardware Specification | Yes | All the experiments have been conducted on a computer equipped with Intel(R) XEON E5-2637 CPU @ 3.5 GHz and 128 Gib of memory. |
| Software Dependencies | No | The paper mentions 'scikit-learn' and 'glucose' but does not specify their version numbers. |
| Experiment Setup | Yes | All the hyperparameters have been set to their default values (100 trees per forest). |