%0 Generic
%A Akkerman, Fabian
%A Ferry, Julien
%A Artigues, Christian
%A Hebrard, Emmanuel
%A Vidal, Thibaut
%D 2025
%T Code, Data, and Experimental Results for "Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods"
%U 
%R 10.4121/f82dcdaa-fc94-43c5-b66d-02579bd3de4f.v1
%K Transparent, Interpretable, Explainable ML
%K Linear programming
%K Machine Learning
%K Ensemble model
%X <p>This dataset contains (1) all code needed to reproduce our results, (2) the 20 data sets on which we report results, and (3) all experiment results, related to the paper: "Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods" as published in TMLR, see: https://openreview.net/forum?id=lscC4PZUE4.</p><p><br></p><p>The code is written in Python 3.12, a README inside the zipped code folder provides more details on setting up and running the code.</p><p><br></p><p>In the zipped data sets folder, we provide a README with more information on our preprocessing steps and links to the orginal sources from which we retrieved the data sets.</p><p><br></p><p>Each experiment is outputted to a JSON file. We include both the results as reported in the paper (the best-found hyperparameter setting) and all experiments related to other hyperparameter settings. The JSON files are organized in zipped folders per experiment type. See the README for further details.</p>
%I 4TU.ResearchData