Computing Abductive Explanations for Boosted Regression Trees
Authors: Gilles Audemard, Steve Bellart, Jean-Marie Lagniez, Pierre Marquis
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Empirical Evaluation The generation algorithm G and the evaluation algorithm E have been assessed on several datasets in order to figure out the extent to which they are practical. Experimental setup The empirical protocol we considered was as follows. We have focused on 10 datasets for regression, which are standard benchmarks found on the web sites... |
| Researcher Affiliation | Academia | 1 Univ. Artois, CNRS, Centre de Recherche en Informatique de Lens (CRIL), F-62300 Lens, France 2Institut Universitaire de France {audemard, bellart, lagniez, marquis}@cril.fr |
| Pseudocode | No | The paper describes the algorithms verbally and with mathematical constraints, but does not include structured pseudocode or algorithm blocks labeled 'Algorithm' or 'Pseudocode'. |
| 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 10 datasets for regression, which are standard benchmarks found on the web sites kaggle (https://www.kaggle.com/), UC Irvine Machine Learning Repository (https://archive.ics.uci.edu/ml/ index.php) or open ML (https://www.openml.org/). |
| Dataset Splits | No | The paper states: 'For each dataset, each boosted tree has been learned from a training set containing 80% of the dataset, and its accuracy was measured as its mean R2 score [Ling and Kenny, 1981] over the remaining 20% of the dataset.' This describes an 80/20 train/test split, but no explicit validation set is mentioned. |
| 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 | Yes | Mg contains MILP constraints and indicator constraints (which are supported by the solver used, namely CPLEX [Cplex, 2009]). |
| Experiment Setup | Yes | All the hyper-parameters of the two learning algorithms have been set to their default values (100 trees per forest, with a depth at most 6 for XGBoost and a number of leaves at most 31 for Light GBM). |